Oral Health Care Provider Model Components

Published 2019

In this module:

This module describes the data, assumptions, and methods used to model the supply of and demand for dentists and dental hygienists. Projections for these oral health occupations were developed by county. These projections were then aggregated to produce estimates at the state and national levels. 

We contacted oral health professional associations to identify the best available data sources, discuss trends affecting workforce supply and demand, and request feedback on preliminary findings. This technical documentation and other HRSA reports do not necessarily reflect the views of the associations, and there may not be clear consensus on all assumptions. Individuals from the following associations participated: 

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

Many of these associations provided information on current professionals and new graduates. The remainder of this module expands on how HWSM models supply and demand for oral health providers.

Modeling Supply 

Sufficient data were available to project the future supply of dentists and dental hygienists, but not dental assistants. Several supply scenarios were modeled. The Status Quo scenario extrapolates current trends in supply determinants:  

  • number and characteristics of current and new providers 
  • hours worked patterns 
  • attrition patterns 

Alternative scenarios modeled the sensitivity of future supply to changes in key trends and assumptions—including (a) ±10% change in annual numbers of new graduates entering the workforce and (b) ±2-year change in observed retirement patterns. 

Estimating the Base Year Workforce Supply 

HWSM supply projections are built on baseline numbers and characteristics of each provider type. Data on dentists come from the ADA 2017 Master File. This file contains demographic data on every individual who completes dental school and categorizes dentists into seven groups:  

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

Dentists in this file who were unlicensed and not retired were treated as inactive. 

Base year demographic information for dental hygienists comes from the ACS. ACS data for 2013-2017 were combined to ensure a sufficient sample size in each state to use sampling with replacement to create a representative population of the national dental hygienist workforce. The size of the sample drawn reflected the state-level estimates of active providers from the 2017 OES. However, the number sampled is scaled to match the national total in the ACS, as the OES may count dental hygienists with multiple positions multiple times, which could produce national estimates that are too large. Individual records that contained information on age, sex, and state of residence from the ACS and the ADA were retained as the base year supply of active hygienists and dentists, respectively. 

To model dentists at different levels of rurality, the ZIP code, the most geographically-specific field in the ADA Master File, is converted into a county for each dentist using Housing and Urban Development’s ZIP-County Crosswalk File.1 The county is converted into one of six designations of rurality according to the CDC’s National Center for Health Statistics Urban-Rural Classification Scheme for Counties.2   

  • Counties classified as “Large central metro” are considered urban. 
  • Counties classified as “Large fringe metro” are considered suburban. 
  • Counties classified as “Medium metro” and “Small metro” are considered towns. 
  • Counties classified as “Micropolitan” and “Non-core” are considered rural.  

Because the ACS does not have a geographic indicator smaller than PUMAs (Public Use Microdata Areas), supply modeling by rurality is not available for dental hygienists.  

Modeling New Entrants 

The number of new dentists projected to enter the workforce from 2018-2030 is based on the latest available survey data from the ADA.3 These data include the sex, race/ethnicity, and state of residence of the 6,238 dentists graduating in 2018 as well as in which of the six NCHS urban-rural classifications the new dentists are working. We apply these distributions to simulate the demographic distribution and geographic location of new dentists graduating. The graduates in each dental specialty were subtracted from total graduates to estimate the number of new general dentists. A breakdown of these graduates by specialty, sex, race/ethnicity, and age is shown in Exhibit 28. Age distributions were calculated using the age at graduation (or completion of advanced education) for all dentists in the ADA Masterfile who graduated since 2010. The 2018 Survey of Allied Dental Education reported the age, sex, race/ethnicity, and state of residence of that year’s 7,385 new dental hygienists.4

Profiles of individual new dentists and dental hygienists entering the workforce each year were simulated using these state, age, race/ethnicity, and sex distributions. HWSM projections assume that the annual number of graduates entering the workforce as well as the age and sex distribution of new oral health professionals remain the same in the future (e.g., 49% of new dentists and 96% of new dental hygienists are female each projection year).  

Modeling Workforce Participation 

Labor force participation rates for oral health professionals age 50 or less by age, sex, race/ethnicity, and occupation were estimated based on 2013-2017 ACS data regarding employment status (active/inactive). Because dental specialties are not recorded in the ACS, all dentists are assumed to have the same workforce participation probabilities, but dental hygienists have different probabilities.  

The estimated probabilities that a dentist retires were calculated by age and dental specialty using age of retirement for all dentists who retired between 2010 and 2017 in the 2017 ADA Masterfile. Because dentists’ retirement patterns differ based on dental specialty, we did not separate them into demographic groups due to sample size concerns. 

Dental hygienists’ probabilities of attrition by age were calculated from the 2013-2017 ACS data. Age cohort differences across years were used to estimate the net number of people leaving the workforce each year. For example, one estimate of net attrition between age 65 and 66 is calculated by comparing active supply of providers age 65 in 2015 versus active supply of providers age 66 in 2016. To increase the data on which estimates were based, age-specific retirement estimates were averaged across the 2013-2017 period. Sample size is insufficient to model dental hygienist attrition by sex or by race/ethnicity. 

Modeling Hours Worked 

Ordinary Least Squares (OLS) estimates of hours worked for each oral health occupation were generated from the latest 5 years (2013-2017) 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 29). Because the ACS does not include information on dentist specialty, the same hours-worked regression coefficients are used for all specialties.  

Oral health professionals in the projections have a calculated number of weekly hours worked based on the OLS regression results. These average hours worked per week differ by age, sex, race, and occupation. As a dentist’s or dental hygienist’s age increases, projected hours worked for that provider can change accordingly. While dentist hours worked data are not available by dental specialty, survey data from the ADA suggests that specialty dentists as a group work a similar number of hours as general dentists.5

The expected number of hours worked per week by each individual is converted to FTE supply by dividing the total person-hours worked by 40. 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 can differ from the actual count of persons employed in the occupation. 

Modeling State-Level Supply and Migration 

HWSM accounts for annual movement of oral health professionals across states in two steps. First, logistic regression on ACS data estimates the probability of migrating to any other state for the under age 50 population as a function of age group, sex, race, the population of the state from which the person moved, and a year indicator. Comparing each person’s move probability with a random number between 0 and 1 determines which providers move each year. The likelihood that each person moving 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 ACS shows 10% of dentists who relocate moving to California, then in HWSM each dentist who moves has a 10% probability of moving to California.  

Second, when an oral health provider moves to a specific state, HWSM then assigns the level of rurality based on the current rurality distribution of the workforce in the state. For example, if in a state 50% of dentists work in a large core metro location as determined by the 2017 ADA Masterfile, then each dentist moving to that state has a 50% probability of being assigned as working in a large core metro area. 

Modeling Demand 

Modeling Annual Visits to an Oral Health Provider 

Prediction equations in HWSM model annual visits to dental hygienists. They also model annual visits to each type of dentist:  

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

These prediction equations were estimated using negative binomial regression with the MEPS Dental Visit Files from 2012-2016. Separate regressions were estimated for children and adults. Separate regressions were also estimated for dental hygienists and for each dentist specialty.  

MEPS does not have pediatric dentists as a unique specialty. When children visited a dentist, MEPS does 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 46% of dental visits by children under 2 years of age were for care provided by a pediatric dentist. The remaining 54% 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. In total, approximately 26% of dental visits by children and adolescents were to a pediatric dentist and 74% were to a general dentist. In HWSM, we use these percentages to model the proportion of dental visits by children and adolescents that likely receive care from general dentists and from pediatric dentists. 

The regressions model the correlation between people’s characteristics and annual use of oral health services. The dependent variable in each regression is annual visits to the oral health provider type. Explanatory variables were the same demographic, economic, health status, and health behavior variables described in Modeling Demand for Health Care Services and Providers for modeling demand for ambulatory care visits. Coefficients from these regressions were applied to the county population files to produce estimates of the expected number of oral health visits to each provider type in each county. 

Data limitations precluded including dental insurance as a predictor of the demand for dental care services. Although dental insurance 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 in lieu of medical insurance coverage. 

The switch in 2019 to modeling demand at the county level created opportunities to better integrate county-level environmental factors into the demand projections. For oral health, this allowed for projecting demand for oral health services taking into consideration estimates of the proportion of the population with access to fluoridated water. Community water fluoridation reduces decay by an estimated 25%.6 Fluoridation rates vary significantly by county. CDC compiles county-level estimates of the number of residents on optimally-fluoridated community water systems. 

From such data, obtained directly from CDC in 2017, the percentage of a county’s residents on fluoridated water is estimated. These values are used to adjust demand for restorative dental services (and, therefore, and demand for the general dentists that primarily provide those services) by a county’s fluoridation rate. Without any adjustment, demand for general dentists implicitly assumes the national average amount of fluoridation exists in all counties. Multiplying unadjusted demand by the ratio of decay (a proxy for demand) expected at a county’s actual fluoridation rate to the decay (or demand) expected at the U.S. average fluoridation rate and then multiplying this ratio by the estimated percentage of general dentists’ time spent on restorations, yields a scalar to adjust for differences in demand for general dentists resulting from differences in fluoridation rates by county.  

Several caveats and limitations must be noted regarding these county-level adjustments. 

  1. Fluoridation rates are positively correlated with population density. The modeled annual visits to a dentist by urban-rural classification indicators might in part capture the effects of lower fluoridation levels on demand for oral health services. They may also capture other factors correlated with population density that affect annual use of services (e.g., less availability of supply so longer distances to travel to receive care). The fluoridation adjustment applied to demand for general and pediatric dentistry services might over-adjust. This means that demand for general and pediatric dentists in less densely populated areas might be overstated and demand in more densely populated area overstated. Consequently, we rescaled the fluoridation-adjusted FTE demand estimates across the NCHS classifications to their levels before fluoridation adjustment. This removes the bias (estimated between 0 and 2.7%, variation across rurality level), while preserving the impact of variation in fluoridation rates across counties that fall into each of the six NCHS classifications. 
  2. We assume no changes in fluoridation over time within each county. This is consistent with stagnant rates of fluoridation over the past decade. 
  3. CDC has a self-reporting system for collecting fluoridation data called Water Fluoridation Reporting System (WFRS). CDC notes that WFRS is reasonably accurate at the state level, but is less reliable at the county level. County-level accuracy varies by state. When water system boundaries and county boundaries diverge, percentages of the water systems customers in each county are estimated by CDC. Additionally, converting number of water system accounts to number of customers served also requires estimation by data collectors at the water systems. When, because of estimation errors, the number of people in a county reported in WFRS to be on optimally-fluoridated community water exceeded total county population estimates, CDC’s correction method was applied.7
  4. WFRS captures information about fluoridation of community water systems. It produces no data about the “small portion”8 of the population that is on unregulated private wells. As a result, estimates of the percentage of counties’ residents on fluoridated water implicitly assume that the portion of the population using well water is not on optimally-fluoridated water. 

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 clinic setting9 , staffing ratios in other settings (e.g., emergency departments) were not developed. To determine provider-to-visit ratios, HWSM projections assume that the demand for oral health services (aggregated for the nation) in the base year is met exactly by the base year supply of providers (see Exhibit 30).  

The Status Quo demand projections hold provider-to-visits staffing ratios unchanged during the projection period. 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 characteristics and socioeconomic status. 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. 

Scenario Modeling Improved Access to Oral Health Services 

Tooth decay is preventable and can easily be treated in the early stages. Untreated dental caries can lead to tooth loss, pain, and infection that can spread to other parts of the body. Nearly 1 in 5 (18.6%) children and adolescents age 5 to 19 has untreated dental caries. Untreated caries rates were especially high for people who are below 100% of the poverty line (24.7%), Black (23.4%), between 100%-199% of the poverty line (22.3%), or Hispanic (21.7%).90 Characteristics associated with lower rates of untreated dental caries include non-Hispanic White (16.7% prevalence) and being 400% or more above the poverty line (9.1% prevalence). Prevalence of untreated caries is even higher among adults age 20-44 (31.6%), adults age 45-64 (27.2%), and adults age 65 years and over (21.8%). Sub-setting on these population characteristics reveals even greater prevalence of untreated caries—reaching 71.7% for Black adults age 45-64 living below 100% of the poverty level.

Demographic and financial metrics directly correlated with prevalence of untreated dental caries are inversely correlated with having visited an oral health provider in the previous year. 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, 91% of children ages 2-17 had a dental visit in the past year. That same percentage was 80.8% for adults ages 18-64 and 82.6% for adults age 65 and over.11 In comparison, for the Hispanic population below 100% of the poverty level, the percentage of people visiting a dentist in the past year is 83.6% for children ages 2-17, 46.2% for adults ages 18-64, and 46.1% for adults age 65 and older. 

The Healthy People 2020 objectives include reducing the proportion of children, adolescents and adults with untreated dental decay by 10%; increasing the proportion of people who used the oral health care system in the past year by 10%; and other goals related to increased access to preventative oral health services.12

The Status Quo demand scenario in HWSM models the continuation of current care use and delivery patterns, which extrapolates under-utilization of oral health services into the future. A hypothetical scenario modeled improved access to oral health services. This improved access 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 that would be likely to occur if access barriers to oral health services were diminished for populations with low rates of using the oral health system. 

Specifically, this scenario models the increase in demand for general and pediatric dentist services and increase in demand for dental hygienist services. It models it 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 urban or suburban areas 
  • with household income above $75,000 (the highest income level modeled in HWSM) 
  • with medical insurance  
    • which is correlated with having dental insurance, as dental insurance is unavailable in HWSM  

If under-served populations used oral health services at rates like a population with few access barriers, then rates of untreated caries to may fall to levels seen among populations with few access barriers. While this scenario does not detail how care might be improved, the 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 DHPSAs 13
  • promoting integration of oral health and primary care practice.14
  • the Perinatal and Infant Oral Health Quality Improvement initiative to improve access to oral health care for pregnant women and infants 15
  • other initiatives to improve access to care for many of the nation’s most vulnerable populations.
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