This analysis is the first to empirically consider a range of correlates for children’s oral health based on a comprehensive conceptual model with nationally representative data. It is intended to provide researchers, advocates and dental professionals with information on factors associated with children’s oral health as reported by their parents. Although our results are provisional, they represent an important step forward since they are based on a comprehensive conceptual model and encompass biological, environmental, social and behavioral correlates that have not been studied together previously. As we discuss below, our findings can be used as a basis for future investigations of specific interventions to improve the oral health of children.
Our goal was to identify factors associated with oral health operating at the child, family, neighborhood and state levels. Several important findings emerged from our analysis. First, we found relatively few significant correlates of oral health at the state level. This is not to say that factors acting at the state level are not important. We found that state-level access to fluoridated water was associated with better oral health for young children. But, for the most part, significant effects were found for variables measured at child, family and neighborhood levels.
Second, we found evidence that most domains in our conceptual model are associated with oral health outcomes for children. Measures of biologic and genetic endowment, physical attributes, development, dental insurance, use of dental care, family composition, family function, socioeconomic status, health behaviors and coping skills, culture, social environment, social capital and physical environment were significant correlates of oral health. Our analysis showed that domain effects were significant at the child, family and community levels. However, we did not evaluate the relative importance of different domains, so further research with multifactorial approaches is encouraged. Such studies can help inform policymakers concerning the domains and levels where interventions would be most efficacious.
Some surprising exceptions to the general finding that most domains mattered were the lack of significant effects of community oral health environment and health care and dental care system characteristics, which would seem to be directly relevant. These domains may be important for older children, an avenue for further research. Another surprise was the direction of the use of dental care effect: compared with children whose last dental visit occurred during the previous year, young children whose last dental visit was more than a year ago or who had never had a dental visit had better perceived oral health. This is likely due to very young children being more likely to be taken to the dentist only if they exhibit recognizable problems with their teeth or specific complaints, and less likely if primary teeth were considered to be temporary or unimportant. Not recognizing that the child’s teeth are in poor condition could result in (i) not taking the child to the dentist and (ii) not rating the oral health as fair or poor.
Primary language spoken at home was an important correlate of children’s oral health. Results indicate children in households where the primary language was not English had about 1.5 times the odds of being in fair/poor oral health as children in English-speaking households. This is particularly striking since the model controls for household income and education and child race/ethnicity, nativity, dental insurance, and use of dental care. If other studies confirm this finding, additional health education and health promotion efforts in culturally appropriate languages may be needed to reach nonEnglish speakers.
Interestingly, we found that children in states with greater income inequality were less likely to have fair/poor oral health, while family income measured at the individual-level was inversely related to worse oral health in our model. Other surveys have also documented that individual-level income is inversely related to poor health in the general population (1
), but no studies have reported the converse association we found at the state level. The state may be too large of a geographic unit for this kind of measure to have much meaning. Alternately, the Gini Index may be acting as a proxy for other factor(s) operating at the state-level not included in our model. Given these interpretational issues, this finding merits further investigation with other data sets.
Dentists traditionally focus on child-level factors (i.e., those pertaining to the patient). Our results suggest that family and neighborhood characteristics may play an important role in relation to children’s oral health outcomes. One avenue for further research is to examine cross-level interaction effects, which were beyond the scope of this study. Further research taking a broader perspective on the domains associated with oral health could ultimately contribute to improved dental health outcomes for children.
This investigation has several limitations. While these limitations necessarily qualify the conclusions that can be drawn from this study, they can also serve to inform future research on the determinants of children’s oral health. An important limitation follows from the cross-sectional design of the NSCH which precludes causal inferences. Consequently, our findings should be viewed as provisional and subject to verification using other data sources. Development of population-based longitudinal surveys of children’s oral health that permit identification of causal relationships, while expensive and difficult undertakings, should be explored to address this issue. At present, no large-scale longitudinal surveys of children’s oral health exist at the national level. If properly implemented, such surveys could provide a basis for improving our understanding of the determinants of children’s oral health.
Our dependent variable is a subjective measure of parent’s report of the condition of the child’s teeth. Although these reports were not validated using dental exams, evidence from other studies suggests that subjective reports of oral health are reasonably valid indicators for many oral health issues, including root canals, fillings, and prostheses, while the evidence is somewhat mixed for caries (47
). More generally, our outcome variable should be viewed as a global health measure that captures parental assessment of multiple aspects of oral health - such as toothaches or swelling, decayed or missing teeth, restorations from past disease, dental esthetics, and functional aspects like chewing ability. It is possible that other sets of explanatory factors would be significant in models predicting different specific oral health problems. The next iteration of the NSCH questionnaire was revised to identify specific oral health problems, such as toothaches, broken teeth, bleeding gums, and decayed teeth or cavities, providing researchers a unique opportunity to assess the factors that may contribute differentially to a variety of important oral health outcomes.
More generally, our study findings are derived from a sample survey, which is subject to various forms of nonrandom error, including nonresponse bias. Survey error is present in any population survey and the NSCH has several methodologies built-in to minimize such biases. These include random selection, assurances of confidentiality for respondents, selecting respondents who were most knowledgeable about the sample child’s health, testing of questionnaire items through focus groups and cognitive interviewing, and incentives to increase response rates.
Finally, although we were able to examine a large number of explanatory factors in our models, there were variables that were identified in our conceptual model (4
) for which data were unavailable. Some domains could only be measured by proxy, including family-level social support and child-level health behaviors and practices. Given the potential importance of these factors, we recommend that future survey questionnaires incorporate more direct measures of these variables. Similarly, our indicators of biological and genetic endowment are also indicators of parental health status, and the two domains could not be disentangled without access to more direct measures of biological and genetic endowment, such as indicators of siblings’ or parents’ oral health status. Here again, questionnaire modifications could fruitfully address these issues.