ABSTRACT
Objectives
The aim of this study was to assess the relative strength of association of four state‐level factors—Medicaid reimbursement, Medicaid adult dental benefit (MADB) generosity, dentist Medicaid participation, and dentist supply—on individual‐level dental care use among children and adolescents in Medicaid.
Methods
This national cross‐sectional study used 2018–2019 National Survey of Children's Health data to estimate dental care utilization among children aged 1–17 enrolled in Medicaid. Subgroup analyses were conducted by child age group. A hierarchical regression approach was used; a series of logistic regression models assessed relative effect sizes among the four state‐level variables.
Results
Among children aged 1–17, and the age 1–3 subgroup, dentist Medicaid participation was positively associated with dental care use. Among children aged 4–11, children in states with Emergency/No dental coverage for adults had greater odds of having a dental visit compared to children in states with Extensive dental coverage. Among adolescents aged 12–17, no state‐level factors were associated with dental utilization.
Conclusions
There was considerable variation by age group in the associations of state‐level factors on dental care use among children and adolescents in Medicaid. Dentist participation in Medicaid was associated with dental care use among very young children. State‐level policy levers have the potential to improve access to dental care for children enrolled in Medicaid, and are critical to achieve improvement toward oral health equity for children.
Keywords: children, dental care access, dentist Medicaid participation, Medicaid, Medicaid dental benefits
1. Introduction
Tooth decay is the most prevalent chronic illness among children [1], and poor oral health in children is associated with missed school and lower academic performance [2, 3] and lower quality of life [4]. In the past decade, dental care use has increased among children in the U.S. regardless of family income [5]. However, despite improvements, children in families with low income who are enrolled in Medicaid are still less likely to receive dental care, and are more likely to have untreated tooth decay, than children in families with higher income and with private insurance [5, 6, 7, 8].
For people with low income who are enrolled in Medicaid, health and dental care access is influenced by system‐ and individual‐level factors. In a conceptual model of healthcare access for populations with low income by Davidson et al., system‐level factors that are particularly critical for individuals with Medicaid include healthcare market factors, such as provider participation in Medicaid and provider supply, and safety net support factors, such as Medicaid payment and coverage [9]. These factors affect whether an individual has coverage for the care that they need, and their ability to find a provider. Four key system‐level factors that have been found to impact access to dental care for Medicaid‐enrolled children include Medicaid reimbursement for dental services, the generosity of dental benefits for Medicaid‐enrolled adults (hereafter referred to as Medicaid adult dental benefit [MADB] generosity), dentist participation in Medicaid, and dentist workforce supply [10, 11, 12, 13, 14, 15, 16]. Several studies have found that increasing state‐level Medicaid reimbursement for dental services is associated with higher dental care utilization among children [13, 15, 17]. The proposed mechanism connecting reimbursement and child dental utilization is via increasing dentist participation. However, there is mixed evidence regarding the degree to which dentist participation acts as a mediator in this relationship [16], and no evidence regarding which types of providers are most likely to change participation behavior after a reimbursement policy change.
Adult MADB generosity may influence child dental care use via spillover effects from parents. The oral health of children and adolescents are directly impacted by the attitudes, behaviors, and oral health of their parents [18, 19], and children whose parents utilize dental care are more likely to receive dental care themselves [20]. Dental insurance coverage is a key driver of dental utilization, yet states are not required to provide dental coverage for Medicaid‐enrolled parents. As a result, there is considerable variation across states in the extent of dental coverage for this population. As of 2019, 18 states provided extensive coverage for Medicaid‐enrolled adults, 16 provided limited coverage, 11 provided coverage for emergency services only, and 5 states provided no dental coverage at all [21]. MADB generosity has been found to impact access to care for Medicaid‐enrolled adults [22], and emerging evidence is suggesting that there are spillover effects of these policies on children [10, 11, 12]. While these studies adjusted for other state‐level factors, such as reimbursement or dentist supply, they did not assess the effects of MADB generosity relative to other state‐level factors that may be related to child dental care use.
Among adults, there is evidence that Medicaid expansion and higher MADB generosity increases dental care use, but only in states with high dentist supply [23]. However, the importance of dentist supply relative to Medicaid factors for children has not been studied. It would be expected that dentist supply could also moderate the relationship between dentist participation and child dental utilization. That is, higher Medicaid participation in a state with a relatively low number of dentists per capita may have less of an impact on child dental utilization than higher Medicaid participation in a state with a higher number of dentists per capita. Overall, little is known about the relationships among Medicaid dental policy‐related factors and their relative effects on child dental utilization. Thus, the aim of this study was to estimate the relative strength of association of four state‐level factors—Medicaid reimbursement, dentist Medicaid participation, MADB generosity, and dentist supply—with individual‐level dental care use among children and adolescents with Medicaid.
2. Methods
This national cross‐sectional study used individual‐ and state‐level data to examine associations between child dental utilization and the four state‐level factors. For child‐level variables, we used pooled data from the 2018 and 2019 waves of the National Survey of Children's Health (NSCH). The NSCH is a national annual household survey of the civilian, noninstitutionalized population that assesses the health and well‐being of children age 0–17, and is administered by the U.S. Census Bureau [24]. Data collection for the 2018 and 2019 waves occurred from June–Dec 2018 and June 2019–January 2020, respectively.
The dependent variable was whether the child had a dental visit in the previous 12 months (Y/N), as reported by their parent. Covariates included child age in years, race/ethnicity (Asian, Black, Hispanic, White, other race, or multiracial per NSCH data user guidelines), sex (M/F, parent‐reported), oral health status (Excellent, Very good, Good, Fair/Poor), parent education (Less than HS, HS, Some college, College degree or higher), child general health status (Excellent, Very good, Good, Fair/Poor), parent employment (Yes if one or both parents were employed, No otherwise), and number of caregivers (One vs. More than one). Covariates were selected based on evidence of association with child dental care use [25, 26, 27].
Independent variables included four state‐level factors: MADB generosity, Medicaid reimbursement, dentist participation in Medicaid, and dentist supply. State‐level MADB generosity was sourced from the Center for Health Care Strategies Inc. [21] The variable was initially categorized as extensive coverage (more than 100 diagnostic, preventive, and restorative procedures with an annual maximum of at least $1000), limited coverage (fewer than 100 diagnostic preventive, and restorative procedures, with an annual maximum of $1000 or less), coverage for emergency services only (relieving pain under emergency circumstances), and no dental coverage (hereafter referred to as none), which have been defined and used in previous literature [23]. Subsequently, states with emergency and no coverage were combined due to the low number of states with no coverage. To ensure stability of MADB generosity during the pre‐survey period, children were excluded if they lived in states that experienced a change in adult dental Medicaid coverage from 2017 to 2019 (N = 3 states: Alaska, Idaho, Illinois), which were the year prior to and the years the surveys were fielded. Dentist Medicaid participation estimates by state in 2017 (the only year available at the time the study was conducted) were sourced from the American Dental Association Health Policy Institute [28], as were estimates of Medicaid reimbursement in 2016 [29]. For this paper, dentist participation in Medicaid was defined as the percentage of dentists in the state who treated (i.e., submitted a claim) 100 or more patients with Medicaid in calendar year 2017 [28]. Children in 10 states were removed from analyses due to missing data on dentist Medicaid participation (Arkansas, District of Columbia, Indiana, Minnesota, Nevada, Nebraska, Pennsylvania, South Carolina, South Dakota, and West Virginia), leaving a total of 38 remaining states in the analytical sample. State Medicaid reimbursement was measured as a percentage of dentist fees, which reflects the “non‐discounted amount charged by dentists for various procedures before network discounts are applied” [29]. These estimates were only available for 2016 at the time of the study but generally do not change considerably year to year so this was considered satisfactory. Dentist supply was sourced from HRSA and defined as the number of dentists per 100,000 population in the state [30].
The study sample consisted of children and adolescents aged 1–17 whose responding caregiver (hereafter referred to as “parent”) reported that the child was currently enrolled in Medicaid or a government assistance program for people with low income or a disability (hereafter referred to as “Medicaid”) and had not had any gaps in coverage in the previous year, which aimed to ensure stability of Medicaid enrollment in the prior year. Analyses were conducted for all children in the analytic sample, and subgroup analyses were conducted for children aged 1–3 and 4–11, and adolescents aged 12–17 [31]. Subgroup analyses were conducted for these three age groups due to the high degree of variation in dental utilization, as well as potential variation in access barriers, among very young children, preschool and elementary school‐aged children, and adolescents. Sampling weights were used to obtain population‐based estimates, which adjust for the complex sampling design, nonresponse, and household probability of selection [32]. Complete case analysis was used, and sample subsetting for Medicaid‐enrolled children was done in accordance with NSCH data user guidance to maintain appropriate weights. As a result of complete case analysis, a total of 32,755 weighted individuals were removed from the final sample due to missing data on at least one variable, which represented 0.2% of the initial eligible sample (weighted N = 16,056,388). The percent of missing values for each variable category ranged from 0% to 0.4%. The final analytical sample included 7905 unweighted and 15,174,400 weighted subjects, including 2,745,185 aged 1–3, 7,427,102 aged 4–11, and 5,002,113 aged 12–17.
Weighted bivariate Chi‐square tests used a Rao‐Scott correction to account for large sample size, while Wilcoxon rank‐sum tests were used for bivariate analysis of continuous variables. A hierarchical regression approach [33] was used whereby a series of 16 logistic regression models were generated to assess relative effect estimates among the four state‐level variables, both alone and for each unique combination of the four variables. Due to multiple levels of covariates (state and individual), a multilevel modeling approach was considered to account for within‐state correlation. However, diagnostics and model exploration showed that the estimated state‐level group variance was near zero, indicating the data were not hierarchical, so standard logistic regression was used instead. All models adjusted for covariates. Models were compared using the Akaike information criterion (AIC) calculated from the Rao‐Scott approximation to the weighted log‐likelihood, as implemented in the “survey” R package. A lower AIC indicates a superior fit; in a model comparison, an AIC of at least 2 units lower is considered a significantly better‐fitting model. For continuous independent variables, odds ratios indicate the odds associated with a 10‐percentage point increase for dentist participation and Medicaid reimbursement, and a 10‐unit increase for dentist supply.
Bivariate analyses, including Spearman correlation and Kruskal–Wallis tests, were conducted to examine associations among the four state‐level variables. Models were also run for children enrolled in private insurance as a robustness check. Following previous studies that have treated MADB generosity as binary (Extensive/Limited vs. Emergency/None), we also ran models this way to examine change in point estimates, which remained very similar. Multicollinearity was checked for all models, and all were deemed satisfactory (VIF < 5). This study was deemed not human subjects research by our University's Institutional Review Board as all data were deidentified.
3. Results
Characteristics and bivariate results comparing children with and without a dental visit are described in Table 1, with the subgroup descriptive and bivariate results presented in Table S1. Approximately 82% of children had a parent‐reported dental visit in the last year. The largest racial/ethnic group was Hispanic/Latino (39%), a majority of the sample had excellent or very good oral health (68%) and general health (84%), most children had more than one caregiver in the home (75%), almost half of sample children's parents had more than a high school education (46%), and most had at least one employed parent (79%). Four in ten children (40%) lived in states that provided emergency or no dental benefits to Medicaid‐enrolled adults.
TABLE 1.
Characteristics of children aged 1–17 enrolled in Medicaid, and their state characteristics, total and by dental care use, 2018–2019 National Survey of Children's Health.
| Characteristic | Overall a | Dental visit in the past year a | ||
|---|---|---|---|---|
| Yes | No | p b | ||
| N = 12,384,957 | N = 2,789,443 | |||
| Weighted N = 15,174,400 | ||||
| (82%) | (18%) | |||
| Age | < 0.001 | |||
| 1–3 | 18% | 54% | 46% | |
| 4–11 | 49% | 89% | 11% | |
| 12–17 | 33% | 86% | 14% | |
| Sex | 0.59 | |||
| Female | 48% | 82% | 18% | |
| Male | 52% | 81% | 19% | |
| Race/ethnicity | < 0.001 | |||
| Asian | 3.0% | 74% | 26% | |
| Black | 21% | 78% | 22% | |
| Hispanic/Latino | 39% | 86% | 14% | |
| Other race or multiracial | 5.6% | 80% | 20% | |
| White | 31% | 79% | 21% | |
| Child oral health status | 0.91 | |||
| Excellent | 35% | 81% | 19% | |
| Very good | 33% | 81% | 19% | |
| Good | 23% | 83% | 17% | |
| Fair/Poor | 9.1% | 82% | 18% | |
| Child general health status | 0.48 | |||
| Excellent | 55% | 81% | 19% | |
| Very good | 29% | 83% | 17% | |
| Good | 13% | 84% | 16% | |
| Fair/poor | 2.7% | 79% | 21% | |
| Parent education | 0.36 | |||
| Less than high school | 19% | 84% | 16% | |
| High school | 35% | 80% | 20% | |
| Some college or associate degree | 28% | 80% | 20% | |
| College degree or higher | 18% | 84% | 16% | |
| Parent employment | 0.09 | |||
| Yes | 79% | 82% | 18% | |
| No | 21% | 79% | 21% | |
| Number of caregivers | 0.10 | |||
| More than one | 75% | 81% | 19% | |
| One | 25% | 84% | 16% | |
| State characteristics c | ||||
| Medicaid reimbursement d | 44.4 (21.6) | 44.4 (22.0) | 43.6 (14.2) | 0.06 |
| Dentist Medicaid participation e | 14.0 (10.0) | 14.0 (10.0) | 14.0 (8.0) | 0.05 |
| Dentist workforce supply f | 53.8 (24.8) | 53.8 (24.8) | 53.0 (24.8) | 0.16 |
| Medicaid adult dental benefit generosity | 0.19 | |||
| None/emergency | 40% | 83% | 17% | |
| Limited | 13% | 78% | 22% | |
| Extensive | 47% | 82% | 18% | |
%; Median (interquartile range).
Chi‐squared test with Rao & Scott's second‐order correction; Wilcoxon rank‐sum test for complex survey samples.
State characteristics summarize characteristics of the states where children in the sample reside.
Medicaid reimbursement as a percentage of dentist fees.
Percent of dentists in the state who treated 100+ children with Medicaid in the last year.
Number of dentists per 100,000 population in the state.
In bivariate analyses, having a dental visit was associated with age and race/ethnicity, and the associations with Medicaid reimbursement (p = 0.06) and dentist Medicaid participation (p = 0.05) approached statistical significance (Table 1). Children aged 1–3 were significantly less likely than older children to have had a dental visit (p < 0.001), and Hispanic children had the highest rate of dental utilization (p < 0.001). Among children aged 1–3, having a dental visit was significantly associated with race/ethnicity (p = 0.03), Medicaid reimbursement (p = 0.03), and dentist Medicaid participation (p = 0.011) (Table S1). Among children aged 4–11, having a dental visit was associated with race/ethnicity (p = 0.003) and child oral health status (p = 0.03). Among adolescents aged 12–17, dental care use was significantly associated with race/ethnicity (p = 0.003), sex (p = 0.026), oral health status (p < 0.001), parent/caregiver education (p = 0.01), and dentist workforce supply (p = 0.029).
Correlations among state‐level variables for the 38 states with complete data are presented in Table 2. There was a positive and significant correlation between dentist participation and Medicaid reimbursement (r = 0.33, p = 0.03), an inverse and significant correlation between dentist participation and workforce supply (r = −0.36, p = 0.02), and very low inverse and non‐significant correlation between dentist workforce supply and reimbursement (r = −0.07, p = 0.65). MADB generosity was not associated with reimbursement nor dentist participation, but was associated with workforce supply (p = 0.01); states with higher MADB generosity had a higher number of dentists per capita than states with lower MADB generosity. States with either limited or no/emergency dental coverage had considerably higher mean reimbursement (51%), on average, than states with extensive coverage (43%), though this difference was not statistically significant (p = 0.13).
TABLE 2.
Correlation among state‐level policy and provider availability factors (N = 38 states with complete data).
| Characteristic | Dentist workforce supply a | Dentist Medicaid participation a | Medicaid adult dental benefit generosity | |||
|---|---|---|---|---|---|---|
| Extensive | Limited | None/emergency | p c | |||
| N = 15 b | N = 8 b | N = 15 b | ||||
| Medicaid reimbursement d | −0.07 (p = 0.65) | 0.33 (p = 0.03) | 42.8 (10.2) | 51.4 (14.3) | 51.2 (11.9) | 0.13 |
| Dentist Medicaid participation e | −0.36 (p = 0.02) | — | 15.8 (5.6) | 17.4 (9.1) | 15.5 (8.1) | 0.90 |
| Dentist workforce supply f | — | — | 65.3 (10.8) | 55.0 (5.4) | 53.9 (10.1) | 0.01 |
Spearman correlation test.
Mean (SD).
Kruskal–Wallis rank sum test.
Medicaid reimbursement as a percentage of dentist fees.
Proportion of dentists in the state who treated 100+ children with Medicaid in the last year.
Number of dentists per 100,000 population in the state.
Multivariable regression results are presented in Tables 3 (full sample) and 4 (age subgroups). Full tables including confidence intervals are provided in Tables S2–S5. Among the full sample, dental care use was associated with both Medicaid reimbursement and dentist participation alone, but the effect size for reimbursement was considerably reduced with the addition of dentist participation, suggesting that participation mediates the relationship between reimbursement and dental care use. Models 4 and 9 had the lowest and comparable AIC values, so Model 4—with dentist participation alone—is considered the most parsimonious given fewer variables. In Model 4, a 10‐percentage point increase in dentist participation was associated with 31% higher odds of a dental visit (OR 1.31, 95% CI 1.09–1.56).
TABLE 3.
Adjusted odds ratios (ORs) for state‐level variables obtained from multivariable logistic regression models (full sample, N = 15,174,400 weighted subjects aged 1–17 from 2018 to 2019 National Survey of Children's Health).
| Characteristic | Model 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Medicaid adult dental benefit generosity | ||||||||||||||||
| Extensive | — | — | — | — | — | — | — | — | ||||||||
| Limited | 1.06 | 0.94 | 1.05 | 1.10 | 0.98 | 0.98 | 1.22 | 1.14 | ||||||||
| None/emergency | 1.21 | 1.08 | 1.11 | 1.28 | 1.06 | 1.16 | 1.34* | 1.27 | ||||||||
| Medicaid reimbursement | 1.14* | 1.13* | 1.08 | 1.14** | 1.08 | 1.13* | 1.09 | 1.06 | ||||||||
| Dentist Medicaid participation | 1.31** | 1.28** | 1.22* | 1.36** | 1.20* | 1.39** | 1.27* | 1.31** | ||||||||
| Dentist workforce supply | 0.97 | 1.03 | 1.01 | 1.04 | 1.04 | 1.14 | 1.05 | 1.12 | ||||||||
| AIC | 7142 | 7145 | 7126 | 7121 | 7149 | 7135 | 7130 | 7151 | 7119 | 7134 | 7127 | 7129 | 7140 | 7126 | 7125 | 7128 |
Note: All models adjust for individual‐level characteristics. Bold indicates the most parsimonious model.
p < 0.05.
p < 0.01.
p < 0.001.
TABLE 4.
Adjusted odds ratios (ORs) for state‐level variables obtained from multivariable logistic regression models (by age subgroup, 2018–2019 National Survey of Children's Health).
| Characteristic | Model 1 | Model 2 | Model 3 | Model 4 | Model 5 | Model 6 | Model 7 | Model 8 | Model 9 | Model 10 | Model 11 | Model 12 | Model 13 | Model 14 | Model 15 | Model 16 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Ages 1–3 (weighted N = 2,745,185) | ||||||||||||||||
| Coverage (2017–2019) | ||||||||||||||||
| Extensive | — | — | — | — | — | — | — | — | ||||||||
| Limited | 1.11 | 0.75 | 1.04 | 1.13 | 0.83 | 0.79 | 1.38 | 1.08 | ||||||||
| None/emergency | 1.47 | 1.04 | 1.15 | 1.49 | 1.00 | 1.11 | 1.65 | 1.38 | ||||||||
| Reimbursement | 1.4** | 1.41** | 1.22 | 1.4*** | 1.24* | 1.42** | 1.24* | 1.21 | ||||||||
| Dentist Medicaid participation | 1.95*** | 1.89*** | 1.60** | 2.11*** | 1.56* | 2.17*** | 1.74** | 1.79** | ||||||||
| Dentist supply | 0.91 | 1.01 | 1.02 | 1.09 | 1.04 | 1.26 | 1.12 | 1.21 | ||||||||
| AIC | 1544 | 1545 | 1511 | 1504 | 1546 | 1518 | 1513 | 1549 | 1499 | 1517 | 1509 | 1508 | 1522 | 1511 | 1503 | 1508 |
| Ages 4–11 (weighted N = 7,427,102) | ||||||||||||||||
| Coverage (2017–2019) | ||||||||||||||||
| Extensive | — | — | — | — | — | — | — | — | ||||||||
| Limited | 1.14 | 1.03 | 1.14 | 1.16 | 1.05 | 1.06 | 1.23 | 1.12 | ||||||||
| None/emergency | 1.66* | 1.52 | 1.59* | 1.72* | 1.51 | 1.58 | 1.77* | 1.65 | ||||||||
| Reimbursement | 1.17 | 1.11 | 1.14 | 1.14 | 1.09 | 1.11 | 1.13 | 1.08 | ||||||||
| Dentist Medicaid participation | 1.25 | 1.15 | 1.11 | 1.17 | 1.07 | 1.20 | 1.06 | 1.11 | ||||||||
| Dentist supply | 0.89 | 1.02 | 0.93 | 0.93 | 1.03 | 1.08 | 0.93 | 1.06 | ||||||||
| AIC | 2617 | 2608 | 2613 | 2616 | 2619 | 2609 | 2611 | 2613 | 2615 | 2617 | 2621 | 2612 | 2615 | 2615 | 2620 | 2618 |
| Ages 12–17 (weighted N = 5,002,113) | ||||||||||||||||
| Coverage (2017–2019) | ||||||||||||||||
| Extensive | — | — | — | — | — | — | — | — | ||||||||
| Limited | 1.18 | 1.13 | 1.18 | 1.18 | 1.21 | 1.13 | 1.32 | 1.39 | ||||||||
| None/emergency | 0.77 | 0.73 | 0.69 | 0.77 | 0.70 | 0.73 | 0.79 | 0.83 | ||||||||
| Reimbursement | 1.02 | 1.06 | 0.96 | 1.04 | 0.97 | 1.06 | 0.96 | 0.95 | ||||||||
| Dentist Medicaid participation | 1.21 | 1.35 | 1.26 | 1.38 | 1.38* | 1.44 | 1.43 | 1.50* | ||||||||
| Dentist supply | 1.06 | 1.00 | 1.08 | 1.15 | 1.00 | 1.10 | 1.15 | 1.12 | ||||||||
| AIC | 2249 | 2252 | 2255 | 2252 | 2255 | 2257 | 2248 | 2257 | 2255 | 2260 | 2254 | 2252 | 2262 | 2253 | 2257 | 2257 |
Note: All models adjust for individual‐level characteristics. Bold indicates the most parsimonious model.
p < 0.05.
p < 0.01.
p < 0.001.
Among children aged 1–3, patterns were similar to the full sample but with larger effect sizes. Model 9 was the most parsimonious, which included Medicaid reimbursement and dentist participation. In this model, a 10‐percentage point increase in dentist participation was associated with 60% increased odds of a dental visit (OR 1.60, 95% CI 1.13–2.26).
Among children aged 4–11, the only state‐level variable associated with dental care use was MADB generosity, where children in states with emergency or no coverage for adults were significantly more likely to have had a dental visit compared to children in states with Extensive coverage. This association was slightly attenuated with the addition of reimbursement and dentist participation. Model 2 was the most parsimonious, which included MADB generosity only. In Model 2, children in states with emergency/no coverage for adults had 66% higher odds (OR 1.66, 95% CI 1.08–2.57) of having a dental visit compared to children in states with Extensive coverage for adults, whereas children in limited coverage states did not have significantly different odds of a dental visit than children in extensive coverage states.
Among adolescents aged 12–17, dental care use was significantly associated with Medicaid participation, though only in models that included reimbursement and MADB generosity. Model 1 was the most parsimonious, which included individual‐level covariates only.
Models with the sample of privately insured children are presented in Table S6. In the most parsimonious model, only dentist supply was associated with having a dental visit and none of the Medicaid‐related factors, so this supported the robustness of results for the sample of Medicaid‐enrolled children.
4. Discussion
This study examined four key Medicaid and provider availability factors together to see how they attenuate each other's associations with child dental utilization. This study provides important new evidence of variation by age group, as most previous studies on dental care access among children and reimbursement or dentist participation have not examined effects by age [13, 14, 15, 16].
Although higher dental care use was associated with higher dentist participation among the full sample of children aged 1–17, subgroup analyses indicated that this pattern was driven by children aged 1–3. This finding corresponds with a study from Indiana that found that Medicaid reforms, including reimbursement increases, had the greatest impact on dental care use among the youngest children [34].
While dentist participation in Medicaid had previously been found to be positively associated with dental care use among children [16], studies frequently used provider enrollment in Medicaid as a proxy for actual participation because data on participation were not readily available [12]. However, the resource used in this study from the American Dental Association Health Policy Institute used national Medicaid claims data to provide state‐level estimates of actual dentist participation in terms of Medicaid patient volume [28]. This adds to the strength of the validity of this study's findings related to Medicaid participation given that it uses a measure of actual provision of dental services to people enrolled in Medicaid.
Our study found that the association with MADB was limited to children 4–11, and the direction of association was contrary to our hypothesis. Children in states with higher MADB generosity were less likely to have had a dental visit than children in states with lower MADB generosity. In contrast, two recent studies found that increasing MADB generosity resulted in higher child dental care use and oral health status, respectively, though they did similarly find the effects to be concentrated among younger children (they used two age groups, ages 1–11, and 12–17) [11, 12]. These previous studies had stronger study designs than the present study; they used quasi‐experimental methods with national survey data and incorporated time‐varying state characteristics and fixed effects to account for potential observed and unobserved state factors that may influence child dental care use. Unobserved factors could include broader economic trends or other potential policy changes in Medicaid or otherwise that would affect families' healthcare seeking behavior. Thus, given the counterintuitive direction of results and the fact that we were unable to account for unobserved and/or time‐varying state factors in this cross‐sectional study, our findings related to MADB should be interpreted with caution.
Regarding associations between the state‐level factors themselves, there was a moderate and statistically significant correlation between Medicaid reimbursement and dentist Medicaid participation. In previous longitudinal studies on the impact of Medicaid fee increases on dentist participation, state‐level fee increases were often coupled with other Medicaid changes, including streamlining administrative requirements for providers and increasing case management services, which made it difficult to tease out the independent effect of higher reimbursement rates on provider participation and dental care use [14, 17]. The one longitudinal study that measured change in provider participation after a reimbursement increase included both general and pediatric dentists in the provider group [14]; however, it is unknown whether fee changes would impact different provider types differently. For example, participation among pediatric dentists may be more sensitive to reimbursement changes than general dentists given the generally higher daily patient volume. The finding that the effect of Medicaid reimbursement was concentrated among very young children suggests a possible pediatric dentist‐specific mechanism, which is important because pediatric dentists are known to have the highest rates of Medicaid participation compared to general dentists or other specialists [35]. The state‐level estimates of Medicaid participation used in this study included general dentists and specialists in aggregate; therefore, we were not able to examine participation among pediatric dentists alone.
Limitations for this study relate to study design and data availability. Most importantly, the cross‐sectional study design cannot assess causality, and estimates may be biased by unobserved state‐level factors. Thus, the results should be interpreted with caution and future studies should utilize quasi‐experimental study designs that aim to disentangle the effects of policy and provider factors on access to care. Survey data are known to have inherent biases, including recall and social desirability bias. Using administrative data to examine patterns of actual dental care use can overcome this limitation, though this type of data source often poses significant cost and data access challenges. The NSCH data do not include information about parents' insurance status, so the hypothesis regarding MADB generosity's impact on child dental care use via parental utilization assumes that Medicaid‐enrolled children have at least one Medicaid‐enrolled parent, which we were unable to verify. Regarding provider availability, we used state‐level estimates but provider distribution is not even across states and therefore may not accurately reflect provider availability in some areas. Children in 10 states were removed from the analyses due to missing state‐level data or recency of dental benefit changes, thus the results may not be representative of the population of children in Medicaid in the U.S. as a whole. Finally, the source for reimbursement for dental services relied on fee‐for‐service rates; however, children in approximately 30 states were enrolled in dental managed care plans and it is not known how closely the plans' reimbursement rates matched fee‐for‐service rates given that information is proprietary and not publicly available.
5. Conclusions
State‐level policy levers have the potential to improve access to dental care for children enrolled in Medicaid, and are critical to achieve improvement toward oral health equity for children. This study found that dentist participation was associated with dental care use among very young children. Improving dental care use by the youngest children in Medicaid has the potential to set them on a trajectory for better oral health into adulthood.
Conflicts of Interest
The authors declare no conflicts of interest.
Supporting information
Data S1: Supporting Information.
Funding: This project was supported by the Health Resources and Services Administration (HRSA) of the U.S. Department of Health and Human Services (HHS) under grant number R40MC41750 in the Maternal and Child Health Secondary Data Analysis Research Program. This information or content and conclusions are those of the author(s) and should not be construed as the official position or policy of, nor should any endorsements be inferred by HRSA, HHS, or the U.S. Government.
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