The Effect of the Supply of Dentists and Dental Hygienists Per Capita
on Oral Health Preventive Care Utilization and Outcomes
AcademyHealth Annual Research Meeting
Boston,, MA
June 27-29, 2010
Tracey Continelli, ABD
Center for Health Workforce Studies
SUNY Albany School of Public Health
http://chws.albany.edu
Percentage of U
U.S.
S adults aged 18 and older with full
dentition by annual family income, 1999*
* Age standardized to the year 2000 U.S. population.
Data source: 1999 Behavioral Risk Factor Surveillance System (BRFSS), Centers for Disease Control and
Prevention.
4.1.3
Percentage of U.S
U S adults aged 50 and older with 21 or more
teeth by education, 1988-1994*
* Age standardized to the year 2000 U.S. population.
Data source: The Third National Health and Nutrition Examination Survey (NHANES III) 1988-1994, National
Center for Health Statistics, Centers for Disease Control and Prevention.
4.2.2
Percentage of U
U.S.
S adults aged 18 and older with full
dentition by demographic variables, 1988-1994*
* Age standardized to the year 2000 U.S. population except for age groups.
Data source: The Third National Health and Nutrition Examination Survey (NHANES III) 1988-1994, National Center
for Health Statistics, Centers for Disease Control and Prevention.
4.1.1
Prevalence of gingival bleeding among U.S. adults aged 20
and older by selected demographic characteristics, 1988-1994
Data source: The Third National Health and Nutrition Examination Survey (NHANES III) 1988-1994, National
Center for Health Statistics, Centers for Disease Control and Prevention.
3.1.1
Previous Oral Health Research
Previous studies have almost exclusively used
data from large national surveys (MEPS,
(MEPS
NHANES, NHIS, CES, or SIPP).
These studies have either analyzed national
trends over time
time, or analyzed the socio
sociodemographic predictors of oral health
outcomes in the population
population.
Li it ti
Limitations
off Previous
P i
Research
R
h
Nearly all oral health datasets lack
geographical identifiers below the regional
level (e.g., Northeast, South, Midwest, West).
Therefore, little local or community level
research
h has
h been
b
conducted.
d t d
Very little research has examined the
relationship
p between the oral health
workforce and oral health measures.
P i
Previous
Studies
St di
 Byck et al. (2002) found that the supply of dentists was
positively associated with the proportion of Medicaid enrolled
children who received dental services across 102 Illinois
counties
ti (b
(butt did nott control
t l for
f relevant
l
t socio-demographic
i d
hi
characteristics in the analyses).
 Allison and Manski (2007) found a positive significant
relationship between dentists per 100,000 population and both
teeth cleaning and dental visits within the past year across 105
Kansas counties
counties, controlling for all other relevant socio
sociodemographic factors.
 No studies have included dental hygienists into the analyses
between oral health workforce supply and oral health measures.
Primary Function of Dental Hygienists
 Provide prophylaxis services (removal of plaque and calculus)
 Applying preventive material to teeth (sealants, fluoride varnishes)
 Provide education on appropriate oral hygiene strategies (flossing,
tooth brushing techniques)
 Taking
a g and
a d developing
deve op g radiographs
ad og ap s (x-rays)
( ays)
 In some States they are legally enabled to perform more extended
functions such as p
placing
g and ppolishing
g amalgam
g restorations,,
administering and monitoring local anesthetics and nitrous oxide, and
preparing clinical and laboratory diagnostic tests for the dentist to
interpret.
Data and Methods
 This study uses 2 separate CDC survey datasets – one MMSA level (N=120), and one
county level (N=223) from the 2004 and 2006 Behavioral Risk Factor Surveillance
System (BRFSS). These are both documented and verified subsets of the 2006 BRFSS
with
i h a sufficiently
ffi i l llarge number
b off cases that
h hhave also
l bbeen weighted
i h d to produce
d
valid,
lid
representative local area estimates. All BRFSS data was then aggregated up at the
weighted unit of analysis (county or MMSA). Counties and MMSAs from 43 different
states are represented.
 Two separate dependent variables were estimated; the percent who had their teeth
cleaned within the past year, and the percent who had no teeth removed due to decay or
disease.
 Because these two dependent variables should causally affect one another, path analysis
was used to estimate both the direct and indirect effects of all independent variables
(socio-demographic and oral health workforce) on each of the dependent variables
variables.
 Dentist counts for the year 2004 were purchased from the ADA at the county level (of
practice). Dental hygienist counts were gathered in 2006 at the zip code level (of
residence) from individual state license files that were then compiled, culled for
duplications and outdated licenses and then matched to their respective MMSA or
county. All other variables were obtained from the two CDC datasets.
Methodology
 Path analysis used for county-level analysis. Multiple linear
regressions used for MMSA-level analysis.
 Sample size needs to be at least 5 to 20 times larger than the number of
estimated paths in path analysis to ensure reliable results (Petraitis et
al.,
l 1996) 43 paths
th total
t t l to
t be
b estimated,
ti t d requiring
i i a minimum
i i
off 215
cases for path analysis. Sufficient for county (N=223), insufficient for
MMSA analysis (N=120).
 There are a total of 936 MMSAs in the U.S., 120 of which were
represented in this analysis. Population size of 900 requires a
minimum sample
p size of 105 and population
p p
size of 1000 requires
q
a
minimum sample size of 106 (Bartlett et al., 2001) for regression
analyses, therefore the MMSA sample size (120) was adequate for
linear regression analysis.
V
Variables
b es
Independent Variables
Health (Percent reporting excellent or very good health)
Married (Percent married)
Minority (Percent minority)
College (Percent completing at least some college)
Income (Percent whose household income is $50,000 or more)
Age (Average age)
) Proxy
y for dental insurance
Insurance ((Percent with health insurance).
Female (Percent female)
DentRate (Number of dentists per 100,000 population)
DH_Rate (Number of dental hygienists per 100,000 population)
-------------------------------------------------------------------------------------------------------------------------------------
Outcome Variable (Proximate)
Cleaned (Percent who had their teeth cleaned within the past year)
-------------------------------------------------------------------------------------------------------------------------------------
Outcome Variable (Distal)
TeethRemoved (Percent who have had no teeth removed due to decay or disease)
P th A
Path
Analysis
l i (Counties)
(C
ti ) : Direct
Di t & Indirect
I di t Effects
Eff t
Minority
Health
.155*
College
.384**
Insurance
Cleaned
TeethRemoved
.409**
.252**
Income
.356**
DH Rate
DH_Rate
DentRate
Married
Age
Female
.169*
Regression
eg ess o Analysis
ys s ((MMSAs):
S s): Dependent
epe de vvariable:
b e: Percent
e ce who
w o
had their teeth cleaned within the past year
Variable
Unstandardized Coefficient / Standard
Error
Constant
4.764 (7.924)
Number of dental hygienists per 100,000 pop .048 (.023)*
P
Percent
t completing
l ti att least
l t some college
ll
-.229
229((.079)**
079)**
Percent reporting good-to-excellent health
.327 (.115)**
Percent minority
.089 (.034)*
Percent with
withhealthinsurance
health insurance
.493
493((.097)**
097)**
Percent household income $50,000 or more .275 (.057)**
Percent female
Not significant
Average age
Not significant
Percent married
Not significant
Number of dentists per 100,000 pop
Not significant
* Significant at or belowthe
below the .05
05 level
** Significant at or belowthe
below the .01
01 level
Regression Analysis (MMSAs): Dependent Variable: Percent who have
had no teeth removed due to decay or disease
Variable
Unstandardized Coefficient / Standard
Error
Constant
Number of dentists per 100,000 pop
Percent married
Percent completing at least some college
Average Age
Percent reporting good-to-excellent health
Percent minority
Percent with health insurance
Percent household income $50,000 or more
Percent female
Percent who had their teeth cleaned within
the
h past year
Number of dental hygienists per 100,000 pop
67.080 (11.641)**
.078 (.032)*
.324 (.091)**
.384 (.064)**
-1.267
1 267 (.205)**
( 205)**
Not significant
Not significant
Not significant
g
Not significant
Not significant
* Significant at or below the .05 level
** Significant at or below the .01 level
Not significant
N
i ifi
Not significant
Conclusions and Limitations
 Supply of dental hygienists per capita consistently affects recent teeth cleaning in both
models; also found to directly affect lack of tooth removal in the population in countycounty
level path analysis.
 Supply of dentists per capita found to affect the lack of tooth removal in MMSA
analysis, although it is consistently unrelated to teeth cleaning.
 Oral health workforce variables exert a significant effect even after controlling for all
other relevant factors and should be included in future oral health analyses.
yg
data was based on pplace of residence,, not employment.
p y
 Limitations: Dental hygienist
However, a representative nationwide survey conducted in 2007 by the Center for
Health Workforce Studies of over 5000 dental hygienists across the U.S. found that 74%
worked in the same county in which they lived and 91% worked in the same MMSA in
which
hi h th
they li
lived.
d C
Currently
tl no comprehensive
h i ddata
t exists
i t for
f the
th employment
l
t location
l ti
of dental hygienists by geography, particularly for smaller geographical units of
analysis.
Future Directions
Data is needed to capture the oral health workforce supply:
 Data on the employment location of dental hygienists.
 Data on the location of survey respondents
respondents’ dental office.
 This would permit the establishment of local commuting patterns for oral health, similar
to David Goodman’s Primary Care Service Areas (PCSAs), especially if the above data
were gathered at the zip code level
level.
Data is needed to capture why supply matters:
 Data on the specific tasks dental hygienists actually perform at work and the level of
supervision under which they perform these tasks. (The Center for Health Workforce
Studies’ 2007 national dental hygiene survey showed that dental hygienists working in
g , than did hygienists
yg
workingg in urban
non-urban areas pperformed more tasks,, on average,
areas, and performed these tasks under less supervision from dentists).
 Data on the amount charged for services, especially across different geographical areas.