Association Between Dental Caries and Socioeconomic Status Among One to Five-Year-Old Native American Children

Abstract

Background: The 2010 Indian Health Service (IHS) oral health basic screening survey (BSS) of oral health status for American Indian and Alaska Native (AI/AN) children ages 1 through 5 years nationwide found substantial differences in caries prevalence between different IHS service areas. The reasons for these differences are not well understood. Socioeconomic status (SES) may be a related factor, as it is well established that SES is a strong predictor of caries prevalence. However, it is possible that some IHS service areas have managed to achieve relatively low caries prevalence despite low SES. Objective: This observational study examined associations between caries prevalence among preschool-aged AI/AN children and county-level SES to identify service areas in which caries prevalence was not well explained by SES. Methods: Two sources of data were used for this study. Caries prevalence data among children ages 1 through 5 years from 76 study sites was drawn from the 2010 IHS BSS. Demographic, economic and social information describing the AI/AN population in the county for each of the 76 sites were selected from the American Community Survey. Associations between county SES characteristics and study site caries prevalence were examined using correlation and linear regression analyses. Results: Of the 76 study sites evaluated during the 2010 IHS BSS, caries outcomes and SES indicators varied widely. The percentage of children having experienced dental decay ranged from 14% to 88%, and percentage of children with untreated decay ranged from 0.5% to 74%. Percentage of decayed teeth overall ranged from 2.7% to 37.9%. Median annual income of the county ranged from $17,778 to $63,000; unemployment ranged from 6.4% to 32.9%, and percentage of the AI/AN population with a bachelor's degree ranged from 3% to 22.5%. Median income, unemployment, and educational all showed some correlation with caries prevalence. Linear regression analysis found that of the SES indicators studied, the SES indicator most predictive of caries prevalence was the percentage of the population having attained a bachelor's degree or a higher level of college education (part R2 11% - 22%). Multivariate linear regression analysis adjusting for median income, % unemployment, and % bachelor's degree found that these three SES indicators taken together can explain 29% of the variation observed in percent of teeth with decay, 21% of the variation in any decay experience, and 16% of the variation in any untreated decay. For many study sites, caries prevalence was much higher or lower than what would be predicted based on SES indicators. Sites with the highest and lowest observed caries prevalence tended to also be the sites with highest and lowest observed-predicted values: the healthiest and least healthy areas remained the healthiest and least healthy even after accounting for SES. Conclusions: Caries prevalence within the IHS BSS communities studied was not always well explained by SES, indicating that other factors may be contributing to caries prevalence. These communities should be studied further to discover what factors may be more related to caries prevalence.