Oral health needs of U.S. children with developmental disorders: a population-based study

We utilized the model of access to healthcare by the NAM [5]. Thus, we included the following indicators in our theoretical framework (see Table 1): barriers of access to healthcare (personal, financial, and structural); utilization of dental services; and outcomes variables (OHN and unmet dental needs).

The Institutional Review Board of Texas A&M University determined that this project “is not research involving human subjects as defined by DHHS and FDA regulations.” The IRB added: “Further IRB review and approval by this organization is not required because this is not human research.” (Correspondence: IRB2020-1004; 9/14/2020).

Specifically, the utilization of dental services was analyzed using questions regarding annual dental provider visits and annual preventive visits in the NSCH. Any annual dental provider visit was further collapsed into two groups: “Yes, saw a dental provider” and “No, did not see a dental provider during the past 12 months.” For annual preventive visit, we used the survey’s question: “During the past 12 months, if a child saw a dental provider for preventive dental services such as check-ups, cleaning, sealants, and fluoride treatment?” We classified the children into two groups: “No, did not see a dental provider for a preventive visit” and “Yes, saw a dental provider once or twice within the past 12 months.”

In terms of structural barriers, two variables were used for geographic location: residence (metropolitan and non-metropolitan) and Census Bureau regions. A Metropolitan Statistical Area is defined by the U.S. Office of Management and Budget as containing an urbanized area with a population of at least 50,000 [24]. In the NSCH, since child’s state of residence was collected as Federal Information Processing Standard (FIPS) State Code, we created four categories for the Census Bureau regions: Northeast (Connecticut, Maine, Massachusetts, New Hampshire, New Jersey, New York, Pennsylvania, Rhode Island, and Vermont); Midwest (Illinois, Indiana, Iowa, Kansas, Michigan, Minnesota, Missouri, Nebraska, North Dakota, Ohio, South Dakota, and Wisconsin); South (Alabama, Arkansas, Delaware, District of Columbia, Florida, Georgia, Kentucky, Louisiana, Maryland, Mississippi, North Carolina, Oklahoma, South Carolina, Tennessee, Texas, Virginia, and West Virginia); and West (Alaska, Arizona, California, Colorado, Hawaii, Idaho, Montana, Nevada, New Mexico, Oregon, Utah, Washington, and Wyoming) [25].

For the financial barriers, since no question was asked about dental insurance, “health insurance coverage within the past 12 months” was used as a proxy and includes two categories: insured all 12 months and uninsured all 12 months. The health insurance types were further divided into four categories: public, private, public and private, and uninsured. Four categories for the Federal Poverty Level (FPL) were used to indicate income/poverty level: 0–99%, 100–299%, 300–399%, and 400% and above.

For the personal barriers, we measured the extent of disability which was developed from parents’ responses to two questions in the NSCH: “Health condition affected ability- How often” and “Health condition affected ability -Extent”. Ability was defined as the child’s ability to do things other children his or her age do. If parents responded that their child’s health condition had no impact on his/her ability, the child was categorized as “never” for the extent of the disability. If they responded as “yes” the health condition affected their child's ability somehow, they were asked to describe the extent into three categories: very little, somewhat and a great deal. Accordingly, the extent of the disability variables included four groups: never, very little, somewhat, and a great deal.

Our dependent variable is the perceived OHN, which is a dichotomous variable that we developed from parents’ responses when asked if their child had any of the following oral conditions during the past 12 months: cavities, bleeding gum, and/or toothache. If the parents’ response was “yes” to any of these conditions, the child was classified as having OHN. The other outcome variable, unmet dental needs, was developed from parents' responses to the question: “During the past 12 months, was there any time when this child needed healthcare, but it was not received?”. If parents’ response was “yes”, parents asked to choose from a list of health care services (medical, dental, mental, hearing, and vision) that a child needed but had not received. However, we did not use unmet dental needs as a dependent variable for bivariate and logistic regression as conducted for OHN because in our prospective, the literature is definitive on the unmet dental needs for CSHCN. However, oral health status measured by OHN rarely were addressed in the literature especially for children with DD as a subpopulation.

Additionally, covariates such as age, race/ethnicity, family structure, guardian education, and household language were developed from items present in the NSCH. Age was developed from a continuous variable (0–17) into three categories based on a phase of dentition: < 6 years old (primary), 6–12 years old (transitional), and 13–17 years old (permanent). Race/Ethnicity was developed from two variables, race and ethnicity, to provide five racial/ethnic categories: Whites, African Americans or Blacks, Hispanics, Asians, and Others. Family structure was collapsed into three categories: two parents, single mother, and others. Guardian education included two categories: less than high school or high school and some college or higher. Household language was classified into 2 groups: English and non-English.

Data were analyzed with IBM SPSS software, version 26. Descriptive statistics and bivariate analysis (Chi-square test) were used to compare oral health status, unmet dental needs, and utilization of dental services between children with and without DD. Additionally, frequency tables were used to summarize sociodemographic factors and factors related to access to health care for our sample of children with DD stratified by OHN status. Multi-variable logistic regression analysis was conducted to examine the association between OHN and each variable related to access to healthcare. We checked for collinearity between variables using the Variance Inflation Factor (VIF) and we conducted variables’ selection model.

To ensure proper variance estimation, statistical estimates were calculated for the complex sample design (to adjust clustering, stratification, and non-response). For the analysis, all variables were weighted to represent the population of non-institutionalized children 0–17 nationally. The child’s weight was composed of a base sampling weight, adjustments for both screener and topical nonresponse, an adjustment for the selection of a single child within the sample household, and adjustments used to control to population counts for various demographics obtained from the 2017 American Community Survey (ACS) one-year data. All percentages, confidence intervals (CI), and p values reflect the sampling weights and are thus generalizable to nationally representative estimates. Adjusted Odds Ratio (OR) and 95% CI were reported.

In terms of oral health status as reported by parents, dental caries are the most prevalent oral diseases among our sample. The prevalence of caries is 16.7% among children with DD compared with 9.9% for children without DD. The prevalence of bleeding gums is 3.5% among children with DD and 1.5% among children without DD. Moreover, the prevalence of toothache is 7.2% among children with DD and 4.1% among those without DD. A significantly higher proportion of children with DD relative to children without DD were found to have OHN (20.3% vs. 12.2%, respectively, P < 0.000; Fig. 1). Furthermore, 3.5% of children with DD compared to 1.2% of children without DD reported having needed health care that was not received (unmet dental need) (Fig. 1). Although the rate of unmet dental needs is relatively low, it is more than twice that for children with DD compared with children without DD.

A higher proportion of children with DD relative to children without DD was found to utilize any dental services in the past 12 months (86.1% vs 76.1% P < 0.000) (Fig. 1). However, there was no difference for preventive dental preventive visits between those with and without developmental disorders (96.8% vs. 96.5%, P = 0.639).

For our sample of children with DD, our bivariate analysis shows no association between OHN and any dental provider visit (86.9.1% vs 85.9.1%, P = 0.643) nor preventive dental visit (96.9% vs 96.7%, P = 0.866) (Table 2).

For structural barriers, differences existed in OHN among children with DD by residence location: 70.2% of children with DD with OHN lived in metropolitan areas versus 74.6% without OHN. In non-metro areas, we found 15.6% with OHN versus 10.5% without OHN (P < 008). Residence by Census Bureau region was not significantly associated with OHN (P = 0.389). Of the four regions, the South accounted for the most children with DD with OHN (40.5%); the Northeast had the fewest (14.4%). However, children with DD who live in the West had a higher proportion of OHN (26.1% with OHN vs. 22.0% without OHN). In contrast, the Midwest had a lower proportion of children with DD with OHN (19.0% with OHN vs 21.1% without OHN).

For financial barriers, we found a statistically significant difference for health insurance coverage between children with DD with and without OHN. For children with DD with OHN, 86.6% were insured the entire past 12 months, compared with 92.7% for children with DD without OHN (P < 0.001). Children who were uninsured the entire past 12 months were more likely to have OHN. The type of health insurance was also significantly associated with OHN (P < 0.001). Children with DD with private insurance had a lower proportion of OHN compared to children with public insurance (38.1% vs 46.3%). Furthermore, children with DD with both private and public health insurance were more likely to have OHN (8.0% with OHN vs 6.1% without OHN). A significant difference was also found for income levels between children with DD with and without OHN (p < . 001). Among children with DD with OHN, 80.9% were below the 400% FPL compared with 68.7% for children with DD without OHN.

For personal barriers, children with DD were classified according to their ability to do things most children of the same age usually do: never affected; affected very little; affected somewhat; and affected a great deal. The results suggested that the more children with DD are affected by their condition, they were more likely to meet the OHN criteria. Specifically, children with DD who are never affected by their condition accounted for 58.5% of children with DD but only 48.9% of those with OHN (p < 0.001). Children with DD whose conditions affected their ability a great deal accounted for 8.5% of children with DD but 12.5% of those with OHN (p < 0.001).

When we examined the association between OHN among children with DD and various potential predictive variables, including sociodemographic variables, through multivariable regression analysis (Table 3), we found that elementary school children (aged 6–12 years) had higher adjusted odds of OHN (AOR: 1.88, 95% CI: 1.21–2.93). We also found that children living in the West region had a statistically significant higher odds of OHN than those living in the Midwest (AOR: 1.61, 95% CI: 1.09–2.37).

Our findings of the high use of any dental services among children with DD were consistent with the finding of Iida et al., who found that CSHCN used more dental care services and were more likely to receive only non-preventive care than children without special healthcare needs (4). This was also confirmed by our finding of a non-significant difference for the use of preventive dental care between children with DD compared with children without DD. There was no significant association between OHN and either any dental visit use or preventive dental visit use among our sample of children with DD. This finding was consistent with the results of Nelson et al. and Iida et al. regarding utilization of dental services for CSHCN [3, 4].

The rate of unmet dental needs among children with DD was nearly three times that among children without DD. We also found that the rate of unmet needs among children with DD at the national level (2.4%) was lower than the rate of unmet needs (20%) of the study conducted by Nelson et al., which was limited to Massachusetts [3]. Moreover, our unmet needs rate among children with DD (2.4%) was also lower than the rate of unmet dental needs among CSHCN (8.9%) that was reported by Lewis et al. [7] using the 2006 NSCH. The discrepancies could be explained by the improvement made in meeting the needs of CSHCN such as services offered through Title V Maternal and Child Health Services Block Grant Program for CSHCN [29].

We also investigated the association with OHN among children with DD for the following barriers: geographic location (structural), health insurance and poverty (financial), and the extent of disability (personal).

Although a higher number of children with DD was found in the South, children living in the West had the highest proportion of OHN (1.61 higher odds of OHN compared to the Midwest). This finding was consistent with the results of a study conducted by Paschal et al. (2016) [11], in which the outcome variable was unmet preventive dental needs.

Urbanicity also played a role in OHN among children with DD in our study. Higher odds of OHN were found among children with DD living in non-metropolitan areas (1.42, 95% CI: 1.02–1.99). This was consistent with what Skinner et al. (2006) found in a study that investigated the effect of rural residence on dental unmet need among CSHCN using 2005 NSCH [30]. They found that CSHCN who lived in rural areas were more likely to have unmet dental needs compared with their urban counterparts.

Children with DD who were living in poverty and uninsured were more likely to have OHN, and this was consistent with the literature [3, 7, 8, 10, 31, 32]. A trend of increased OHN with an increased level of poverty was found in our study. Our results of higher odds of OHN with increasing levels of poverty were consistent with Nelson et al. [3], Lewis et al. [7], and Sannicandro et al. [10] regarding poor oral health and greater unmet needs for CSHCN from low-income families. We also found that public health insurance covered a large segment of children with DD (36.8%); nevertheless, the type of health insurance was not associated with increased odds of OHN. Our finding was consistent with Lewis et al. [7] who found that public insurance such as Medicaid and CHIP was not associated with unmet dental needs after adjusting for other confounding factors. McManus et al. [33] also confirmed no association between public health insurance eligibility and unmet preventive care needs.

Higher odds of OHN were found among children who were considerably affected by their condition. These results confirmed what has been reported in the literature regarding the association of condition severity/degree of the disability and OHN or unmet needs among CSHCN [4, 7, 34]. Our result was consistent with Sannicandro et al. finding that CSHCN who had a moderate or severe disability were more likely to have unmet dental needs [10]. Future research to identify, by a medical diagnosis, conditions that adversely affect the functional ability of children with DD is essential.

The study had several limitations. First, this cross-sectional study allowed us to examine associations but not causation, and temporal association was not determined. However, our findings illustrated valuable direction toward future research and targeted public health efforts toward prevention and intervention strategies for the severely affected subgroup of CSHCN. Second, many of our variables including the outcome variable “OHN” were collected through parents’ self-reported data, which were subject to various biases such as recall, reporting, and social desirability. No verification of oral health by calibrated examiners was conducted. Another limitation was that we used health insurance as a proxy for dental health insurance since there was no question in the survey about dental insurance. Generally, the percentage of children without dental insurance is twice that of children without medical insurance [35, 36]. Thus, using health insurance was a suboptimal substitute. Last, there was no verification of the parents’ reported diagnosis of DD among respondents to the survey. However, there is a notable consistency of the prevalence of individual DDs between the results of the NSCH and other nationally representative surveys, such as the National Health Interview Survey (NHIS) [20].

Our study had, however, multiple strengths. To our knowledge, this was the first study to measure the OHN of children with DD using a nationwide sample. Most studies investigated either an individual disorder or a broader group of CSHCN that included other medical conditions such as asthma, diabetes, blood disorders, and cancers. Although these conditions put children under the umbrella of special health care needs, they do not share a common risk of developmentally affected/delayed growth status. Second, our study also looked at the OHN of children with DD by geographic regions. Additionally, the NSCH included a large sample size of representative participants of children with DD from each state, which allowed us to perform robust analysis. Our findings could help policymakers focus efforts or target populations with the highest OHN by regions or to investigate factors related to the high OHN among these populations.