Measuring the quality of dental care among privately insured children in the United States

Sung Eun Choi; Elsbeth Kalenderian; Sharon‐Lise Normand

doi:10.1111/1475-6773.13713

. 2021 Jul 29;57(1):137–144. doi: 10.1111/1475-6773.13713

Measuring the quality of dental care among privately insured children in the United States

Sung Eun Choi ^1,^✉, Elsbeth Kalenderian ^1,^2,³, Sharon‐Lise Normand ^4,⁵

PMCID: PMC8763286 PMID: 34327703

Abstract

Objective

To examine whether quality of dental care varies by age and over time and whether community‐level characteristics explain these patterns.

Data Source

Deidentified medical and dental claims from a commercial insurer from January 2015 to December 2019.

Study Design

A retrospective cohort study. The primary outcome was a composite quality score, derived from seven dental quality measures (DQMs), with higher values corresponding to better quality. Hierarchical regression models identified person‐ and zip code–level factors associated with the quality.

Data Collection/Extraction Methods

Continuously enrolled US dental insurance beneficiaries younger than 21 years of age.

Principal Findings

Quality was assessed for 4.88 million person‐years covering 1.31 million persons. Overall quality slightly improved over time, mostly driven by substantial improvements among children aged 0–5 years by 0.153 points/year (95% confidence interval [CI]:0.151, 0.156). Quality was poorest and declined over time among adolescents with only 20.5% of DQMs met as compared to 42.6% among aged 0–5 years in 2019. Dental professional shortage, median household income, percentages of African Americans, unemployed, and less‐educated populations at the zip code level were associated with the composite score.

Conclusion

Quality of dental care among adolescents remains low, and place of residence influenced the quality. Increasing the supply of dentists and oral health promotion strategies targeting adolescents and low‐performing localities should be explored.

Keywords: access to care, dental quality of care, health services research, oral health, regional variation

What is known on this topic

Tooth decay is the most common chronic disease among US children, yet is often neglected, leading to substantial decreases in children's quality of life and up to 10 million missed school days.
Due to lack of standardization and implementation of diagnostic codes in dentistry, administrative claims data remain the only data that can be utilized to measure quality of care in dentistry today.
While geographic variation in children's dental care utilization has been documented cross‐sectionally using simple metrics, such as annual dental visits, nationwide overall patterns, and trends of dental quality of care have not been extensively investigated using quality metrics.

What this study adds

A composite measure of dental quality was developed based on professionally promulgated recommendations and used to identify systematic person and geographic features impacting the quality.
Quality disparities exist among commercially insured children by age group and by place of residence.
To improve quality of dental care among commercially insured children, oral health promotion strategies should be explored, targeting low‐performing localities and adolescents.

1. INTRODUCTION

Tooth decay is the most common chronic disease among US children. ¹ , ² With considerable efforts to increase access to care, dental utilization among children has improved in the last decade, yet remains one of the greatest unmet children's health needs. ³ , ⁴ , ⁵ Large demographic and geographic variations in dental utilization exist among children. ⁶ , ⁷ , ⁸ These variations in dental care need to be considered in a broader context to identify whether shortfalls in dental care reflect failures of dental insurance, contribution of other factors, or both. Moreover, identification of high‐need subgroups, such as those reside in underserved areas, may require more targeted policy responses.

Interest in quality improvement in health care has been rising in the United States. ⁹ , ¹⁰ Measuring the quality of care and using those measurements to promote improvements in care delivery are well‐practiced in medicine ¹¹ , ¹² and are gaining traction in dentistry both domestically and globally. ¹³ , ¹⁴ Quality measures are tools to quantitatively assess how well we are doing with respect to the delivery and outcomes of dental care. The dental profession has many quality measures, ranging from concordance with clinical practice guidelines developed by professional dental organizations to reaching federal targets, such as Healthy People 2030. ¹⁵ In 2008, the American Dental Association (ADA) formed the Dental Quality Alliance (DQA) to advance performance measurement as a means to improve oral health, patient care, and safety through a consensus‐building process. ¹² The National Quality Forum, in collaboration with the DQA, hosts a wide range of dental quality measures (DQMs) that are evidence‐based oral health care performance measures. ¹⁶

Given large variations in dental utilization among children and the relatively recent development of quality measures in dentistry, quantifying the quality of dental care using DQMs ¹⁷ is an important first step. Information derived from DQMs could provide a nationwide assessment of variation and longitudinal trends in dental quality of care and identify subpopulations in need of quality improvement. Evidence suggests that adolescents, recognized as having distinctive needs pertaining to oral health, have difficulties accessing dental care. ¹⁸ , ¹⁹ Community‐level factors, such as living in rural areas and high levels of poverty, have been associated with dental utilization among Medicaid‐enrolled children. ²⁰ However, we do not know if community‐level factors impact dental utilization or quality in commercially insured children. ²¹

The main goals of this study were to determine whether overall quality of dental care has changed over time and whether quality and longitudinal trends varied among age groups. Because neighborhood factors may affect dental practice patterns, our secondary goal was to assess the associations between community‐level characteristics and quality.

2. METHODS

2.1. Data sources and study population

We used deidentified medical and dental claims data from a US commercial insurer for the period January 1, 2015, through December 31, 2019. DQM‐related person‐year observations were measured from 2016 to 2019, and data in the preceding year were used to identify care continuity (2015 data were used to measure care continuity in 2016). We identified continuously enrolled dental insurance (dental fee‐for‐service [FFS], Dental Maintenance Organization [DMO], or preferred provider organization [PPO]) beneficiaries, who were younger than 21 years of age. Continuous enrollment was defined as having at least 11‐month enrollment per year with gaps of ≤1 month. Methods followed the Strengthening the Reporting of Observational Studies in Epidemiology reporting guideline.

2.2. Measures

We used quality indicators promoted by the ADA that included a set of evidence‐based oral health care performance measures. ¹⁶ Pediatric DQMs include eight measures: periodic oral evaluation, at least two topical fluoride application for high‐risk individuals (individuals who have received caries treatment in the reporting year and in the three prior years), care continuity, usual source of services, caries risk documentation, number of emergency department (ED) visits for caries‐related reasons, and follow‐up after ED visits within 7 and 30 days of the visit (Table S1). ¹⁶

Our primary outcome was a composite measure of overall quality of care constructed from seven binary‐valued DQMs using an item response theory (IRT) model (Text S1). ²² We standardized the composite score to have a mean of 0 and a standard deviation of 1 such that higher scores corresponded to better quality. A Spearman rank correlation coefficient between the composite measure and the number of ED visits (excluded from the initial composite construction) was estimated to quantify the validity of the score. To help with interpretation of the composite score, given it is scaled to have mean 0 and variance 1, we mapped specific values of the composite score to the expected percentage of the DQMs met.

Our explanatory variables included person‐level variables that may affect the quality measures through their association with patients' adherence to recommended care as well as zip code–level health care infrastructure and socioeconomic characteristics plausibly associated with the composite measure. Zip code was chosen as the geographic unit of analysis because dental practice patterns vary by contextual neighborhood factors that could be captured at the zip code level.

Person‐level variables included age groups (0–5, 6–12, and 13–20 years), sex, type of dental insurance plan (FFS [freedom to choose providers and beneficiaries pay dentists directly for services], DMO [limited flexibility to choose only in‐network providers, but less out‐of‐pocket cost than PPO], PPO [most costly premium with freedom to choose providers in or out of network]), and calendar year associated with the index claim. Other demographic and socioeconomic status of individuals, such as race/ethnicity, that are potentially associated with the quality were not available in the data. ²³ We identified zip codes, where beneficiaries reside, based on Zip Code Tabulation Area. Zip code–level variables were constructed using the Area Health Resource File and American Community Survey data and could change over time: median household income, Gini index (a standardized measure of income inequality with 0 indicating a completely equal society and 1 indicating a completely unequal society ²⁴ ), percentages of African Americans, urban population, unemployed, populations in poverty, and less than high school diplomas. ²⁵ , ²⁶ Dental Health Professional Shortage Area (none, partial, or whole county ²⁷ ) was the only variable designated at the county level.

2.3. Statistical analyses

We estimated hierarchical linear regression models relating the composite quality score with person‐level and zip code–level covariates. Random intercepts for counties and zip codes within counties permitted variation in the composite score for geography. The hierarchical model empirically estimates the within‐ versus between‐group components of variation in the composite score. Continuous variables were rescaled to be centered around the means. In a first set of models, only person‐level covariates (age, sex, insurance plan, year, and interaction of age and year) were included. The random intercepts (representing average zip code quality for aged 0–5 years with PPO plan in 2016) were allowed to vary across zip codes nested within counties. In a second set of models, we added zip code–level covariates. The variance inflation factors (VIFs) test was used to assess multicollinearity, and the covariates with the highest VIFs (urban and poverty) were removed. The best‐fitting models were selected based on the Akaike information criterion values. To characterize the geographic variation, we examined the estimated variance components and computed intraclass correlation coefficients. All models were estimated using R.

3. RESULTS

3.1. Characteristics of study population

Our cohort comprised 1.31 million persons younger than the age of 21 years across the United States, totaling 4.88 million person‐years during the study period. The sample included 1803 counties with at least one person‐year observation in the 50 states and the District of Columbia. Over the study period, 53.9% of the person‐year observations had none of the DQMs met, 44.8% received a routine oral evaluation, and 35.2% continued to receive oral evaluation in two consecutive years (Table 1).

TABLE 1.

Composite quality score and average performance on dental quality measures

Quality Metric	Composite quality score (% of DQMs met)	Periodic oral evaluation (n = 1.3 M)	Care continuity (n = 1.1 M)	Usual source of care (n = 1.1 M)	Caries risk documented (n = 1.3 M)	Topical fluoride application ^a (n = 0.2 M)	Number of ED visits ^b (n = 2153)	Follow up after ED visit ^b within (n = 2153)
Quality Metric	Composite quality score (% of DQMs met)	Periodic oral evaluation (n = 1.3 M)	Care continuity (n = 1.1 M)	Usual source of care (n = 1.1 M)	Caries risk documented (n = 1.3 M)	Topical fluoride application ^a (n = 0.2 M)	Number of ED visits ^b (n = 2153)	7 days	30 days
Overall	0.000 ± 0.001 (25.5%)	44.8	35.2	25.1	0.6	24.0	1.14 ± 0.41	40.3	52.2
Age
0–5	0.188 ± 0.001 (32.6%)	61.7	37.4	30.9	1.5	38.7	1.14 ± 0.43	50.8	62.7
6–12	0.156 ± 0.001 (30.3%)	50.2	41.8	31.5	0.9	33.4	1.13 ± 0.39	51.4	63.4
13–20	−0.143 ± 0.001 (20.8%)	37.9	30.3	19.6	0.3	15.3	1.15 ± 0.41	31.5	43.3
Sex
Male	0.002 ± 0.001 (25.6%)	44.7	35.2	25.5	0.6	24.3	1.13 ± 0.38	43.0	54.7
Female	−0.002 ± 0.001 (25.4%)	44.9	35.3	24.7	0.6	23.6	1.16 ± 0.44	37.4	49.4
Insurance plan
PPO	0.084 ± 0.001 (28.1%)	49.1	39.2	27.5	0.7	25.0	1.13 ± 0.39	43.4	56.5
DMO	−0.206 ± 0.001 (19.6%)	37.2	24.0	19.9	0.7	14.4	1.15 ± 0.43	40.7	55.2
FFS	−0.307 ± 0.001 (15.9%)	28.3	21.6	16.2	0.3	16.6	1.03 ± 0.19	34.5	44.8
Year
2016	0.001 ± 0.001 (25.6%)	45.5	35.2	25.2	0.5	21.4	1.13 ± 0.39	39.8	49.4
2017	−0.011 ± 0.001 (25.2%)	44.6	34.5	24.7	0.6	22.9	1.15 ± 0.41	39.7	52.3
2018	−0.016 ± 0.001 (24.9%)	43.6	34.5	24.6	0.6	25.7	1.15 ± 0.42	41.5	54.7
2019	0.028 ± 0.001 (26.3%)	45.6	36.6	26.0	0.8	26.9	1.13 ± 0.42	40.3	52.4

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Note: Entries are percentages of eligible populations for dichotomous outcomes and means±SD for continuous variables (composite score and number of ED visits).

^{^a}

At least two topical fluoride application conditioning on individuals on high risk (defined as individuals who have received caries treatment [a set of identified procedure codes] in the reporting year and for up to three prior years).

^{^b}

Conditioning on individuals ever visited emergency department (ED).

Abbreviations: DMO, Dental Maintenance Organization; DQM, dental quality measures; ED, emergency department; FFS, fee‐for‐service; PPO, preferred provider organization; SD, standard deviation.

When constructing the composite score, quality metrics most discriminating of dental quality in the IRT model, suggesting a quality measure that has a high ability to differentiate dental quality among children, were routine oral evaluation and care continuity (Text S1). The composite quality scores were skewed toward poor quality of dental care (Figure S1) with approximately 25.5% of the DQMs met (Table 1). The Spearman rank correlation coefficient between the score and the number of ED visits was −0.02 (95% confidence interval [CI]: −0.03, −0.01) suggesting that as the composite increased the number of ED visits decreased.

3.2. Variations and trends in quality of dental care

In most states, dental care quality was poorest for adolescents (Table 1 and Figure S2); the overall quality score for adolescents was −0.143 (95% CI: −0.144, −0.142) (corresponding to 20.8% of DQMs met), substantially lower than 0.188 (95% CI: 0.185, 0.190) and 0.156 (95% CI: 0.155, 0.158) (approximately 30% of DQMs met) among children aged 0–5 and 6–12 years, respectively.

Longitudinal changes in the composite score also varied among age groups. The mean composite score improved from 0.001 (95% CI: −0.001, 0.002) in 2016 to 0.028 (95% CI: 0.026, 0.030) in 2019 (25.6%–26.3% of DQMs met) mostly owing to substantial improvement in quality among children aged 0–5 years from 0.025 (95% CI: 0.020, 0.030) in 2016 to 0.50 (95% CI: 0.488, 0.501) in 2019 corresponding to 27.3%–42.6% of the DQMs met (Table 1 and Figure 1). The quality score improved annually for children aged 0–5 years (0.155 points/year [95% CI: 0.152, 0.157]) and did not change among school‐aged children (0.007 points/year [95% CI: 0.004, 0.010]) (Table 2). Adolescents were the only age group that experienced annual decreases in the quality by 0.018 points/year (95% CI: −0.020, −0.015) (Table 2 and Figure 1). Adolescents experienced decreases in all individual measures, except for caries risk documentation and topical fluoride application (Figure S3).

Overall composite quality score by year and age group. Data points are mean values with 95% confidence intervals [Color figure can be viewed at wileyonlinelibrary.com]

TABLE 2.

Point estimates from liner mixed effects regression model assessing the associations between the composite score and person‐ and county‐level covariates

Variables	Without community‐level characteristics	With community‐level characteristics
Variables	Estimate	Estimate
Person‐level covariates
Intercept (age 0–5 under PPO in 2016)	−0.041 (−0.053, −0.030)	−0.058 (−0.104, −0.013)
Age 6–12	0.172 (0.167, 0.177)	0.172 (0.167, 0.177)
Age 13–20	−0.057 (−0.062, −0.052)	−0.057 (−0.062, −0.052)
Male	−0.002 (−0.004, 0.001)	−0.002 (−0.003, 0.001)
Insurance (FFS)	−0.273 (−0.277, −0.270)	−0.273 (−0.277, −0.270)
Insurance (DMO)	−0.340 (−0.348, −0.333)	−0.339 (−0.347, −0.331)
Annual change age 0–5	0.153 (0.150, 0.155)	0.153 (0.151, 0.156)
Annual change age 6–12	0.007 (0.004, 0.010)	0.007 (0.004, 0.010)
Annual change age 13–20	−0.018 (−0.020, −0.015)	−0.017 (−0.020, −0.015)
Zip code‐level covariates
African Americans (in 10 percentage points)		−0.012 (−0.015, −0.009)
Less than high school education (in 10 percentage points)		−0.020 (−0.027, −0.013)
Unemployed (in 10 percentage points)		−0.009 (−0.015, −0.003)
Median income (in thousands)		0.002 (0.001, 0.002)
Gini index		0.017 (−0.005, 0.039)
Dental professional shortage (partial county)		−0.015 (−0.025, −0.006)
Dental professional shortage (whole county)		−0.042 (−0.058, −0.026)
Random effects	Zip code variance: 0.086	Zip code variance: 0.070
	County variance: 0.027	County variance: 0.027
	Residual variance: 0.944	Residual variance: 0.946
R ² (%) ^a	13.0	12.7

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Note: Entries are point estimates with 95% confidence intervals. Point estimates represent changes in the quality score per one‐unit changes in the covariates. In case of “less than high school education” zip code–level covariate, with 10 percentage point increase in the composition of population with less than high school education at the zip code level, the quality score decreased by 0.020 (95% CI: −0.027, −0.013).

Abbreviations: DMO, Dental Maintenance Organization; FFS, fee‐for‐service; PPO, preferred provider organization.

^{^a}

Variance explained by the entire model.

Average quality scores ranged from −0.89 to 1.67 (0%–76.7% of DQMs met) at the county level and from −1.31 to 2.88 (0%–100%) at the zip code level (Figure S4). The intraclass correlation coefficients were 8.6% for zip codes and 2.7% for counties. In localities with dental professional shortage designations, lower median income, or higher percentages of African Americans, less than high school education, and unemployed, children experienced lower quality of dental care (Table 2).

4. DISCUSSION

Using a composite quality of dental care score, focusing on process and access to care, we found that even within the same payment system, dental quality of care varied among age groups and across geographic areas of residence. Children aged 0–5 years experienced substantial improvement in quality over the study period, potentially owing to the national efforts around early childhood caries prevention. These efforts include educational programs for parents and national health promotion and prevention initiatives providing anticipatory guidelines for health professionals (including dental providers, physicians, nurses, dietitians, and others), such as Bright Futures. ²⁸ , ²⁹ , ³⁰ , ³¹

However, adolescents received the poorest quality of dental care with no improvement over time. These gaps in dental quality by age and place of residence among commercially insured children inform the need for policy responses beyond providing dental insurance. Compared with other age groups, receipt of preventive services in both dental and primary care has remained low for adolescents, ³² which is consistent with a recent study indicating only minimal improvement in the oral health of adolescents within the last decade. ³³ Adolescents are recognized as having distinctive needs for dental care due to a potentially high risk of oral diseases with increased sugar intake and nicotine initiation. ¹⁸ The self‐concept development process, emergence of independence to seek care or avoid it, and the influence of peers were found to be a few of the psychodynamic factors impacting underutilization of preventive services among adolescents. ³⁴ Thus, adolescents require a tailored approach to motivate them about their oral health. Primary care providers should be encouraged to provide oral health education, risk assessments, and preventive care, such as applying fluoride varnish. Moreover, oral health promotion strategies, targeting lifestyle changes (e.g., dietary choices and use of tobacco), need to be considered to improve oral health of this age group.

Given that measurable neighborhood characteristics explained some of the variations in quality of dental care among commercially insured children, community‐based interventions could be implemented with a goal of bringing these localities to the level of the higher performing communities. For instance, states may provide more resources and monitor outcomes more closely in low‐performing communities when implementing statewide performance‐improvement initiatives. Those communities may be targeted for demonstrations aimed at improving preventive dental care for children.

While geographic variation in children's dental care utilization has been documented cross‐sectionally, ⁶ nationwide overall patterns and trends of dental quality of care among US children have not been extensively investigated using evidence‐based quality indicators. Other studies have assessed validity of DQMs using process‐of‐care indicators, ¹³ , ³⁵ but to our knowledge, this is the first study in the field of dentistry that has developed a composite measure of dental quality based on professionally promulgated recommendations and used the composite measure to identify systematic person and geographic features impacting the quality.

Our study has some limitations. Clinical quality measures are suggested to encompass five domains (process, access, outcome, structure, and patient experience) in the United States, ³⁶ and seven domains (patient safety, effectiveness, efficiency, patient‐centeredness, equitability, timeliness, and access to care) based on working definition of quality of dental care globally. ¹⁴ Because the majority of current DQMs are based on process and access measures, mainly due to lack of standardization and implementation of diagnostic codes in dentistry, they may not adequately represent the true quality of dental care. ¹⁷ As quality measures evolve in dentistry, attention needs to be focused on developing measures that are diagnosis‐ and patient‐centered, such as electronic measures (e‐measures) that can be deployed through the electronic health records (EHRs) at dental practices or digital quality measures that would be more accurate and person specific. ³⁷ , ³⁸

Although our study population represents approximately 3.5% of children covered by commercial dental insurance plans in the United States, our results may not be generalizable beyond the study population, especially to children who are publicly insured or uninsured. Due to lack of diagnostic codes in dental claims, the impact of individual or composite quality measures on health outcomes cannot be validated. However, prior studies provide evidences on the effectiveness of individual quality indicators on dental outcomes using EHRs. ³⁵ Our study did not account for the correlation induced by multiple observations for an individual (random effects at individual level) due to the computational burden, and we may have overstated the precision of some estimates. Like with any observational studies, there may be unmeasured variables that may confound the association between covariates and quality of care, such as other demographic, socioeconomic status, and patient perception (e.g., dental anxiety). In particular, our data do not contain race/ethnicity information, which has been an important stratification factor in oral health disparities in children. ³⁹ Thus, our study may not explain the variation by these unmeasured characteristics. Lastly, because zip code–level covariates represent a zip code average, interpretation of associations with quality cannot be made at the individual level.

While the overall quality of dental care for children improved between 2015 and 2019, variations in quality exist by age and geographic location. Adolescents continue to have lower dental quality of care relative to children at other age groups, and place of residence matters even under the same health insurance system. Policy interventions to improve the quality and oral health of the children residing in these low‐performing localities and adolescents should be examined.

CONFLICTS OF INTEREST

The authors declare no conflicts of interest.

Supporting information

Table S1. Dental Quality Alliance (DQA) pediatric quality measures and procedure codes associated with quality measures.

Text S1. Composite dental quality of care score description.

Figure S1. Distribution of Composite Quality Score by age group.

Figure S2. Trends in the average composite score by age group and state.

Figure S3. Performance on quality metrics by age and year.

Figure S4. Distribution of quality of dental care across counties and zip codes.

Click here for additional data file.^{(10.1MB, docx)}

ACKNOWLEDGMENTS

Research reported in this publication was supported by the Harvard School of Dental Medicine Initiative to Integrate Oral Health and Medicine. The content is solely the responsibility of the authors and does not necessarily represent the official views of Harvard School of Dental Medicine. We acknowledge Drs. Kathe P. Fox and Nathan Palmer in the Department of Biomedical Informatics at Harvard Medical School for providing data and technical help with this work.

Choi SE, Kalenderian E, Normand S‐L. Measuring the quality of dental care among privately insured children in the United States . Health Serv Res. 2022;57(1):137‐144. 10.1111/1475-6773.13713

Funding information Harvard School of Dental Medicine Initiative to Integrate Oral Health and Medicine

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30. Henry JA, Muthu MS, Swaminathan K, Kirubakaran R. Do oral health educational programmes for expectant mothers prevent early childhood caries? ‐ systematic review. Oral Health Prev Dent. 2017;15(3):215‐221. [DOI] [PubMed] [Google Scholar]
31. American Academy of Pediatrics . Bright futures. https://brightfutures.aap.org/Pages/default.aspx. Accessed April 7, 2021.
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33. Centers for Disease Control and Prevention . Dental caries in permanent teeth of children and adolescents. 2019. https://www.cdc.gov/oralhealth/publications/OHSR-2019-dental-caries-permanent-teeth.html. Accessed March 5, 2021.
34. AAPD . Adolescent oral health care. 2017. https://www.aapd.org/globalassets/media/policies_guidelines/bp_adoleshealth.pdf. Accessed August 21, 2020.
35. Kumar SV, Bangar S, Neumann A, et al. Assessing the validity of existing dental sealant quality measures. J Am Dent Assoc. 2018;149(9):756‐764. [DOI] [PMC free article] [PubMed] [Google Scholar]
36. AHRQ . NQMC measure domain framework. https://www.ahrq.gov/gam/summaries/domain-framework/index.html. Accessed August 21, 2020.
37. Hamlin B, O'Kane M, Barr M, Cotton P. COVID‐19 underscores the need for digital quality measurement. Health Aff. 2020. 10.1377/hblog20200824.987845. [DOI] [Google Scholar]
38. Kalenderian E, Obadan‐Udoh E, Yansane A, et al. Feasibility of electronic health record‐based triggers in detecting dental adverse events. Appl Clin Inform. 2018;9(3):646‐653. [DOI] [PMC free article] [PubMed] [Google Scholar]
39. Han C. Oral health disparities: racial, language and nativity effects. SSM Popul Health. 2019;8:100436. [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Table S1. Dental Quality Alliance (DQA) pediatric quality measures and procedure codes associated with quality measures.

Text S1. Composite dental quality of care score description.

Figure S1. Distribution of Composite Quality Score by age group.

Figure S2. Trends in the average composite score by age group and state.

Figure S3. Performance on quality metrics by age and year.

Figure S4. Distribution of quality of dental care across counties and zip codes.

Click here for additional data file.^{(10.1MB, docx)}

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PERMALINK

Measuring the quality of dental care among privately insured children in the United States

Sung Eun Choi, MS, PhD

Elsbeth Kalenderian, DDS, MPH, PhD

Sharon‐Lise Normand, PhD

Abstract

Objective

Data Source

Study Design

Data Collection/Extraction Methods

Principal Findings

Conclusion

What is known on this topic

What this study adds

1. INTRODUCTION

2. METHODS

2.1. Data sources and study population

2.2. Measures

2.3. Statistical analyses

3. RESULTS

3.1. Characteristics of study population

TABLE 1.

3.2. Variations and trends in quality of dental care

FIGURE 1.

TABLE 2.

4. DISCUSSION

CONFLICTS OF INTEREST

Supporting information

ACKNOWLEDGMENTS

REFERENCES

Associated Data

Supplementary Materials

ACTIONS

PERMALINK

RESOURCES

Cite

Add to Collections

PERMALINK

Measuring the quality of dental care among privately insured children in the United States

Sung Eun Choi, MS, PhD

Elsbeth Kalenderian, DDS, MPH, PhD

Sharon‐Lise Normand, PhD

Abstract

Objective

Data Source

Study Design

Data Collection/Extraction Methods

Principal Findings

Conclusion

What is known on this topic

What this study adds

1. INTRODUCTION

2. METHODS

2.1. Data sources and study population

2.2. Measures

2.3. Statistical analyses

3. RESULTS

3.1. Characteristics of study population

TABLE 1.

3.2. Variations and trends in quality of dental care

FIGURE 1.

TABLE 2.

4. DISCUSSION

CONFLICTS OF INTEREST

Supporting information

ACKNOWLEDGMENTS

REFERENCES

Associated Data

Supplementary Materials

ACTIONS

PERMALINK

RESOURCES

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