This study was conducted from December 2019 to June 2020. Data from the National Child Health Survey (NSCH) for the year 2018 was used. The NSCH is a screening for various developmental disabilities that provides data on different cross-sectional aspects of children’s lives, including physical and mental health, parental health, access to health care, family and environment social. . Of the 176,052 addresses sampled in all 50 states and the District of Columbia, NSCH included completed interviews with a parent or other caregiver from a nationally representative sample of 30,530 non-institutionalized children ages 0-17 and 520 to 796 participants per state. . The survey was conducted as a mail and online survey administered by the Data Research Center for Child and Adolescent Health (DRC) in partnership with the MCHB and the US Census Bureau. A weighted overall response rate of 43.1% was achieved. NSCH data is publicly available on the Census Bureau’s NSCH page. Further information on the methodology and sample selection is available on the DRC website (childhealthdata.org).
We identified children with DD based on the definition established by the American Academy of Pediatrics (AAP) [22, 23]. The child was included in the DD group if they had any of the following: Autism Spectrum Disorder (ASD), Down Syndrome (DS), Attention Deficit Disorder (ADD/ ADHD), cerebral palsy (CP), intellectual disabilities (ID), epilepsy, Tourette’s syndrome, developmental delay, learning disabilities, behavioral and conduct disorders and speech disorder. We determined that 6,501 children met this definition.
We used the NAM access to care model . Thus, we have included the following indicators in our theoretical framework (see Table 1): barriers to access to care (personal, financial and structural); use of dental services; and outcome variables (OHN and unmet dental need).
The Institutional Review Board of Texas A&M University has determined that this project “is not research involving human subjects as defined by DHHS and FDA regulations.” The IRB added: “Further review and approval of the IRB by this organization is not required as this is not human research.” (Correspondence: IRB2020-1004; 09/14/2020).
Use of dental services
Specifically, dental service utilization was analyzed using questions regarding annual dental provider visits and annual preventive visits in the NSCH. Any annual visit to a dental provider was further divided into two groups: “Yes, saw a dental provider” and “No, did not see a dental provider in the past 12 months. For the annual preventive visit, we used the survey question: “In the past 12 months, if a child has seen a dental provider for preventive dental services such as checkups, cleanings, sealants and fluoride treatment? We classified the children into two groups: “No, did not see a dentist for a preventive visit” and “Yes, saw a dentist once or twice in the last 12 months”.
Barriers to accessing oral care
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. . In the NSCH, since the child’s state of residence was collected as a Federal Information Processing Standard (FIPS) status 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) .
For financial barriers, since no questions were asked about dental insurance, “health insurance coverage in the past 12 months” was used as a proxy and includes two categories: insured every 12 months and uninsured every 12 months. Types of health insurance 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 personal barriers, we measured the extent of disability that was developed from parents’ responses to two NSCH questions: “Health condition affected ability – How often” and “The health condition affected ability – Degree”. Ability was defined as the child’s ability to do things that other children their age do. If the parents answered that their child’s medical condition had no impact on his or her abilities, the child was classified in the “never” category for the extent of the disability. If they answered ‘yes’ the condition affected their child’s ability in some way, they were asked to describe the extent of this in three categories: very little, quite a bit and a lot. Thus, the range of disability variables comprised four groups: never, very little, a little, and a lot.
Our dependent variable is perceived OHN, which is a dichotomous variable we developed from parents’ responses when asked if their child had any of the following oral conditions in the past 12 months: cavities, bleeding gums and/or toothache. If the parents’ answer was “yes” to any of these conditions, the child was classified as having an OHN. The other outcome variable, unmet dental needs, was constructed from parents’ responses to the question: “During the past 12 months, was there a time when this child needed treatment, but he did not receive any? “. If the parent’s answer was “yes,” parents were 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 the dependent variable for the bivariate and logistic regression as conducted for OHN, because in our prospective, the literature is definitive on unmet dental needs for the CSHCN. However, oral health status measured by OHN has rarely been 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 dentition phase:
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 dental use. dental services between children with and without DD. Additionally, frequency tables were used to summarize sociodemographic factors and factors related to health care access for our sample of children with DD stratified by OHN status. A multivariate logistic regression analysis was conducted to examine the association between OHN and each variable related to access to health care. We checked the collinearity between the variables using the variance inflation factor (VIF) and we performed a variable selection model.
To ensure correct variance estimation, statistical estimates were calculated for the complex sample design (to adjust for clustering, stratification and nonresponse). For the analysis, all variables were weighted to represent the population of non-institutionalized children aged 0 to 17 at the national level. The child weight was composed of a base sampling weight, adjustments for filter and timeliness nonresponse, an adjustment for the selection of only one child within the household sample size and adjustments used to control population counts for various demographics obtained from the 2017 U.S. Community Survey (ACS) one-year data. All percentages, confidence intervals (CI), and p-values reflect sampling weights and are therefore generalizable to nationally representative estimates. Adjusted odds ratio (OR) and 95% CI were reported.