X. Oral Health Care Provider Model Components

Published 2022

In this module:

This module describes the data, methods, and assumptions used in the Health Workforce Simulation Model (HWSM) to generate projections of supply and demand for dentists and dental hygienists. The module also describes modeling of demand for dental assistants, but insufficient data are available to model supply.

As part of HRSA’s efforts to improve workforce projections, the research team conducts periodic outreach to the stakeholders. The goals of outreach efforts are to (1) identify the best available data sources, and (2) discuss trends affecting workforce supply and demand. Individuals from the following associations have provided input, including data, regarding the modeling efforts:

  • American Dental Association (ADA)
  • American Dental Assistant Association (ADAA)
  • American Dental Education Association (ADEA)
  • American Dental Hygiene Association (ADHA)
  • Dental Assisting National Board (DANB)

This document, workforce projections, and our modeling approach do not necessarily reflect the views of the aforementioned associations. Additionally, clear consensus on all assumptions may not exist.

The latest year for which reliable data are available is the “base year”. The base year currently is 2020. The period from the base year through the last year for which projections are made is the “projection period”. The projection period currently is 2020-2035.

The remainder of this module summarizes data and assumptions for modeling supply of and demand for oral health providers.

Modeling Supply

The microsimulation approach to modeling supply begins by creating a database representative of dentist and dental hygienist supply in the base year. Simulations to project future supply consist of the following steps:

  1. Add new entrants to the oral health workforce each year
  2. Remove those who leave the workforce during the year
  3. Adjust hours worked based on demographics (especially age) of oral health providers in the new year
  4. Adjust for oral health providers moving between states during the year

Details of the supply modeling process are found in the Supply Modeling Overview module. The data, methods, and assumptions specific to dentists and dental hygienists are found in the following subsections.

Estimating the Base Year Workforce Supply

We build supply projections from the amounts and characteristics of dentists and dental hygienists in the base year. Data on dentists are from the base year ADA Master File. This file contains data on the age, sex, and home state of every individual who completes dental school. Each dentist belongs to one of the following groups:

  • general dentists
  • orthodontists
  • pediatric dentists
  • oral surgeons
  • periodontists
  • endodontists
  • all other dentists

We treat dentists in this file who are unlicensed and not retired as inactive.

Base year data on the age, sex, and home state of dental hygienists is from the American Community Survey (ACS). The ACS samples Americans and collects yearly vital information to include detailed housing and socioeconomic data. We use data on the dental hygienists in the most recent 5 years of the ACS to create a population database that represents the national dental hygienist workforce. We draw a sample of hygienists from the ACS data for each state. Members of the ACS data could be chosen as many times as they are selected at random. The sizes of the state samples are based on the relative number of active providers between the states as reported in the base year Occupational Employment and Wage Statistics (OEWS) from the Bureau of Labor Statistics (BLS). The total number drawn across all states matches the national total number of hygienists in the ACS. This is because the OEWS may count dental hygienists more than once if they have more than one job. So, using OEWS state totals could produce national estimates that are too large.

To model dentists by metropolitan versus non-metropolitan designation, the ZIP codes in the ADA Master File are converted into a county for each dentist using Housing and Urban Development’s ZIP-County Crosswalk File.1 Counties are converted into one of six categories according to the Centers for Disease Control and Prevention’s (CDC’s) National Center for Health Statistics Urban-Rural Classification Scheme for Counties.2 We treat providers from counties classified as “Micropolitan” and “Non-core” as providing services in non-metropolitan areas. Dentists from all other types of counties are treated as providing services in metropolitan areas. Because the ACS does not have a geographic indicator smaller than PUMAs (Public Use Microdata Areas), supply modeling by rurality is not possible for dental hygienists.

Modeling New Entrants

Estimates of the number and characteristics of new dentists projected to enter the workforce each year and for each dental specialty are based on the latest available survey data from the ADA.3 These data include the sex, race/ethnicity, and state of residence of the dentists graduating in the base year (6,665 for 2020) as well as whether they are providing services in metropolitan or non-metropolitan areas. The number of new general dentists equals total graduates minus the number of graduates in all dental specialties. The percentages of new dentists of each type of specialty, sex, race/ethnicity, and age are shown in Exhibit X-1. We calculated the age distribution at graduation (or completion of advanced education) using data for all dentists in the ADA Masterfile who graduated since 2011. The percentages of new dental hygienists (7,325 in 2020) in each category of age, sex, race/ethnicity, and state of residence come from the latest Survey of Allied Dental Education.3

We simulate the profiles of individual new dentists and dental hygienists entering the workforce to match the proportions of each sex, race/ethnicity, age group, and state of residence (and metropolitan versus non-metropolitan, for dentists) found in the ADA data. The Status Quo supply projections model that the annual number of graduates entering the workforce as well as the age and sex distribution of new oral health workers remain the same in the future (e.g., 52% of new dentists and 95% of new dental hygienists are female each projection year).

Modeling Workers Leaving the Workforce

In the simulation, workers have a chance of leaving the workforce in any projection year if they are older than age 50. We estimated the probability that a dentist leaves the oral health workforce in a given projection year by dentist age and specialty using data in the 2020 ADA Masterfile on age of retirement for all dentists who retired between 2011 and 2020. Insufficient sample size prevents the further breakdown of dentist retirement probabilities by sex, race/ethnicity, or metropolitan versus non-metropolitan location.

We calculated the annual probability that a dental hygienist stops practicing, based on age, using the 2016-2020 ACS data. We compared age cohorts across years to estimate the net number of people leaving the workforce each year. For example, one estimate of the net number of retirements between age 65 and 66 is calculated by comparing active supply of providers aged 65 in 2018 versus active supply of providers aged 66 in 2019; another estimate is found by repeating this process with the 2019 and 2020 data. To increase the amount of data on which retirement probabilities are based, we average these age-specific probability estimates from each pair of back-to-back years among the 5 most recent years of ACS data. Insufficient sample size prevents the further breakdown of dental hygienist retirement probabilities by sex or race/ethnicity.

Modeling Hours Worked

Estimates of hours worked for dentists and hygienists are generated with Ordinary Least Squares (OLS)4 regressions using data from the most recent 5 years of ACS data. The dependent variable is total hours worked in the previous week; explanatory variables are age group, sex, race/ethnicity, and a calendar year indicator (Exhibit X-2). Because the ACS does not include information on dentist specialty, the same hours-worked regression coefficients are used for all specialties. Separate regressions are run for dentists and hygienists.

The weekly hours worked used in the workforce simulations then are the predicted values for a worker’s sex, age, and race/ethnicity generated using the estimated regression coefficients. As dentists’ and dental hygienists’ ages increase across the projection period, their weekly hours worked change accordingly. Dentists’ hours worked data are not available by dental specialty because the ACS does not track dental specialty.

The total expected number of hours worked per week for all workers in a projection year in the simulation divided by 40 is the number of FTEs supplied. This creates a uniform standard of 1 FTE as working 40 hours per week regardless of the occupation. As a result, the initial FTEs of an occupation may differ from the actual count of persons employed in the occupation.

Modeling State-Level Supply and Migration

The model accounts for annual movement of oral health workers between states in two steps. First, logistic regression5 using 2016-2020 ACS data estimates the probability of a provider under the age of 50 migrating to another state based on their age group, sex, race/ethnicity, the population of the state from which the person moved, and a year indicator. Comparing each person’s move probability to a random number between 0 and 1 determines which providers move each year. The likelihood that each person who moves will relocate to a specific state is based on the proportion of people moving to that state as observed in ACS data. For example, if 10% of dentists who relocated according to ACS moved to California, then each dentist who moves in the simulation has a 10% probability of moving to California. This approach ensures that cross-state migration has no impact at the national level in terms of the number and characteristics of providers.

Second, when a dentist or hygienist moves to a specific state, the model then assigns them a value of metropolitan or nonmetropolitan location designation equal to the proportion of dentists or hygienists practicing in metropolitan and non-metropolitan areas of the new state. For example, if 70% of dentists in a moving dentist’s new state work in a metropolitan area (per the latest ADA Masterfile), then the moving dentist has a 70% probability of being assigned a value of metropolitan in the simulation because of the move.

Supply Scenarios

A Status Quo scenario models the supply implications if current patterns continue through the projection period. Specifically, this scenario holds constant:

  • number and characteristics of current and new providers
  • patterns of hours worked
  • patterns of workers leaving the workforce

Alternative scenarios allow us to gauge the sensitivity of future supply to key trends and assumptions. These scenarios reflect future uncertainty. These scenarios include:

  • 10% higher annual numbers of new graduates entering the workforce
  • 10% lower annual numbers of new graduates entering the workforce
  • Workers retire two years earlier than current patterns
  • Workers retire two years later than current patterns

Concerns about provider burnout throughout the health care workforce have been prevalent in recent years, including among the dentist and hygienist workforces.6 7 8 9 Hence, there is likely higher probability of oral health providers accelerating retirement versus delaying retirement.

Modeling Demand

Modeling Annual Visits to an Oral Health Provider

Prediction equations in HWSM model annual visits to a dentist office based on the person’s socioeconomic characteristics, and health related risks and behaviors. Separate equations model annual visits to each type of dentist:

  • general or pediatric dentist
  • endodontist
  • oral surgeon
  • orthodontist
  • periodontist
  • other type of dentist

These prediction equations are estimated using negative binomial regression10 with the Medical Expenditure Panel Survey (MEPS) Dental Visit Files from 2015-2019. Separate regressions are estimated for children and adults. The dependent variable in each regression is annual visits to the oral health provider type. Explanatory variables are the same demographic, economic, health status, and health behavior variables described in the Demand Modeling Overview module for modeling demand for ambulatory care visits. Coefficients from these regressions are applied to the county population files to produce estimates of the expected number of oral health visits to each provider type in each county.

Unfortunately, dental insurance is unavailable as a predictor of the demand for dental care services. Although dental insurance status is available in MEPS, this information is unavailable in the files used to construct the population database. In the MEPS-based regressions, the influence of dental insurance on use of oral health services is reflected in the regression intercept and other explanatory variables such as presence of medical insurance (which is likely positively correlated with having dental insurance). In comprehensive testing, predictions of oral health care utilization among the population generally were not improved by using dental insurance coverage as a predictor instead of medical insurance coverage.

MEPS has limited and inconsistent data availability across the years for type of providers seen. We were able to identify visits to general dentists (including pediatric dentists in the 2018 and 2019 MEPS) and visits to the other dental specialists based on the combined 2015-2019 MEPS data. To determine some of the other types of providers seen during dental visits we use services provided as a proxy for the dental provider seen. For example, visits for cleaning teeth are proxies for dental hygienist visits; visits for root canals are proxies for endodontists; visits for oral surgery are proxies for dental surgeons; visits for orthodontia services are proxies for orthodontists; visits for periodontal/gum related services are proxies for periodontists.

When children visited a dentist in 2015-2017, MEPS did not indicate whether services were provided by a pediatric dentist or by a general dentist. Information provided by ADA, based on ADA’s survey of dental practices, indicates that about 43% of dental visits by children under 2 years of age were for care provided by a pediatric dentist. The remaining 57% of visits were to a general dentist. For children ages 2 to 4, approximately 40% of dental services were provided by a pediatric dentist. This percentage falls to 23% for children and adolescents ages 13 to 17. HWSM uses these percentages to estimate the number of dental visits by children and adolescents to general dentists and pediatric dentists in 2015-2017. A study of the pediatric dentist workforce concluded that supply of pediatric dentists was growing at a faster rate than demand for pediatric dental care, due to the low birthrate and relatively stable number of children, but that demand for pediatric dentists could grow substantially if a larger portion of young children shifted care from general dentists to pediatric dentists.11

Modeling Oral Health Provider Staffing

The simulated demand for dental services is translated to demand for providers through the national provider-to-visit ratios. Because dental services are delivered mainly in a dental office setting12 , HWSM does not model demand for oral health care in other settings (e.g., hospital inpatient and emergency setting). Thus, the small amount of oral health workforce time provided in non-office settings is included with demand for office-based oral health providers.

To determine provider-to-visit ratios, we divide the number of each type of provider in the base year by the estimated number of visits to that type of provider (Exhibit X-3). The base year visits are expected values in the absence of COVID-19, as the health care use patterns are based on 2015-2019 data that predates COVID-19. Applying these patterns to the 2020 population provides estimates of what oral health care use would have been in 2020 absent COVID-19.

Data sources like MEPS do not indicate whether a dental assistant provided care during a patient visit. Hence, HWSM models demand for dental assistants growing at the same rate as demand for overall dentists. That is, we estimate demand for dental assistants as 1.664 times the number of dentists.

Estimates of demand for oral health service delivery in each county and state model the level of care if people in that county or state had access to and used oral health services at the national rate for a population with similar socioeconomic characteristics and health risks and behaviors. That is, national ratios (by specialty) of dentists-to-dental visits (excluding teeth cleaning) in the base year are applied to the projected visits in future (projection) years to determine demand projections for dentists. The ratio of dental hygienists-to-teeth cleaning visits are applied to projected tooth cleaning visits to determine the future demand for dental hygienists.

  • 5Encyclopedia.com. Logistic Regression. Published May 14, 2018. Accessed September 23, 2022.
  • 6Patel BM, Boyd LD, Vineyard J, LaSpina L. Job Satisfaction, Burnout, and Intention to Leave among Dental Hygienists in Clinical Practice. J Dent Hyg. 2021;95(2):28.
  • 7Moro J da S, Soares JP, Massignan C, et al. Burnout Syndrome among Dentists: A Systematic Review and Meta-Analysis. Journal of Evidence-Based Dental Practice. Published online April 2, 2022:101724. https://doi.org/10.1016/j.jebdp.2022.101724
  • 8Kulkarni S, Dagli N, Duraiswamy P, Desai H, Vyas H, Baroudi K. Stress and professional burnout among newly graduated dentists. J Int Soc Prevent Communit Dent. 2016;6(6):535. https://doi.org/10.4103/2231-0762.195509
  • 9Amanda Knutt R, CST M, Jaymi-Lyn Adams R. Compassion Satisfaction, Compassion Fatigue, and Burnout among Dental Hygienists in the United States. Journal of Dental Hygiene (Online). 2022;96(1):34-42.
  • 10Negative Binomial Regression. NCSS, LLC; 2022. Accessed September 24, 2022. (PDF - 375 KB)
  • 11Surdu S, Dall TM, Langelier M, Forte GJ, Chakrabarti R, Reynolds RL. The pediatric dental workforce in 2016 and beyond. The Journal of the American Dental Association. 2019;150(7):609-617.e5. https://doi.org/10.1016/j.adaj.2019.02.025
  • 12Institute of Medicine. Improving Access to Oral Health Care for Vulnerable and Underserved Populations. National Academies Press; 2012.

Demand Scenarios

As with other health occupations, HWSM models demand for oral health providers under two scenarios—a Status Quo scenario and a Reduced Barriers scenario.

The Status Quo scenario models the recent (2015-2019) national patterns of how people seek oral health services, based on their characteristics, as continuing across the projection period. This scenario captures geographic variation in demographics, health risk factors, disease prevalence, insurance coverage, level of rurality, and household income that can affect demand for oral health services. Similarly, the scenario captures population growth and aging—and the associated implications for disease prevalence and other health risk factors—over the projection horizon. The Status Quo scenario assumes national demand (in the absence of COVID-19) equal to national supply in 2020. Thus, this scenario evaluates whether the nation’s future oral health workforce is sufficient to provide at least the base year level of care.

The Reduced Barriers scenario estimates the number of oral health worker FTEs required if populations that historically faced barriers to accessing oral health services demonstrated care use patterns comparable to populations perceived to have fewer barriers to accessing care. The American Student Dental Association states: Barriers to care include anything that limits or prevents people from receiving adequate health care. In the case of dental care, the most common are financial hardship, geographic location, pressing health needs and poor oral health literacy. Language, education, cultural and ethnic barriers may compound the problem.13

This scenario does not model 100% detection and treatment of unmet oral health needs. It does, however, model the increase in visits to oral health providers likely to occur if access barriers to oral health services were reduced for populations with lower rates of using the oral health system. It models if everyone in the U.S. used oral health services at the same rate as a population with the lowest level of barriers to accessing care. This population is:

  • non-Hispanic White
  • living in metropolitan areas
  • with medical insurance (which is correlated with having dental insurance, as dental insurance is unavailable in HWSM)
  • with household income above $75,000 (the highest income level modeled in HWSM)

The Reduced Barriers scenario for oral health providers differs from the Reduced Barriers scenario modeled for other health occupations by adding a household income component. Our analysis of MEPS finds that after controlling for medical insurance coverage, household income is only weakly correlated with use of most health care services. What little income effect exists is often a negative correlation, meaning higher income is associated with lower use of services. However, higher household income is associated with greater use of oral health services.

Tooth decay is preventable14 and can easily be treated in the early stages.15 Untreated dental caries can lead to tooth loss, pain, and infection that can spread to other parts of the body. Household income (as measured by poverty level) as well as minority race/ethnicity are associated with higher rates of tooth decay.16 In the latest National Center for Health Statistics analysis (using data for 2015-2018), 13.2% of children and adolescents aged 5 to 19 had untreated dental caries. Untreated caries rates were especially high for people who were living below 100% of the poverty line (18.7%), Black (15.7%), between 100%-199% of the poverty line (16.9%), or Hispanic (14.1%). Characteristics associated with lower rates of untreated dental caries include non-Hispanic White (11.9% prevalence) and being 400% or more above the poverty line (5.2% prevalence). Prevalence of untreated caries is even higher among adults aged 20-44 (25.9%), adults aged 45-64 (25.3%), and adults aged 65 years and over (20.2%). Sub-setting on these population characteristics reveals even greater prevalence of untreated caries—reaching 58.2% for Black adults aged 45-64 living below 100% of the poverty level.

The characteristics associated with high levels of untreated decay were also associated with lower probabilities of a dental visit in the past year (based on 2018 data).17 The population with the highest usage level is the non-Hispanic White population with household earnings exceeding 400% of the poverty level. Among this population, 82.2% of children aged 2-17 had a dental visit in the past year. This value was 80.0% for adults aged 18-64 and 83.4% for adults aged 65 and over. In comparison, for the Hispanic population below 100% of the poverty level, the percentage of people visiting a dentist in the past year was 60.8% for children aged 2-17, 46.4% for adults aged 18-64, and 40.8% for adults aged 65 and older.

The Reduced Barriers scenario reflects national goals to improve access to affordable, high-quality care. HRSA programs designed to improve access to the oral health system and increase preventative oral health care include:

  • supporting state efforts to address oral health shortages in Dental Health Professional Shortage Areas (DHPSA)
  • supporting loan repayment programs to assist dental faculty development and place National Health Service Corps dentists and dental hygienists in DHPSAs18
  • promoting integration of oral health and primary care practice19
  • the Perinatal and Infant Oral Health Quality Improvement initiative to improve access to oral health care for pregnant women and infants20

Additionally, the Reduced Barriers scenario illustrates that demand for oral health providers would increase as the nation strives to achieve the Healthy People 2030 objectives—which include reducing the proportion of adults with active or untreated dental decay21 ; reducing the proportion of children and adolescents with active or untreated decay22 ; reducing the proportion of persons who are unable to obtain or delayed in obtaining necessary oral health care23 ; and other goals related to increased access to preventative oral health services.24

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