The rationale for using a geography-based
method to identify facilities with critical
shortages of RNs is that recruiting and
retention difficulties at the facility
level will be strongly influenced by geographic
context (e.g., availability of RNs in
the immediate geographic area). Certain
types of facilities (e.g., long-term care
facilities, publicly sponsored facilities)
will have greater relative difficulty
in obtaining and retaining adequate numbers
of RNs in the presence of geographic shortages,
but when numbers of RNs available at the
local level are adequate to meet the needs
of all facilities, inter-facility competition
should be a less important factor. Facilities
in communities with an adequate supply
of RNs may face difficulties in attracting
and retaining RNs due to issues related
to organizational culture and management
practices, but the NELRP program is not
intended to address these difficulties.
Potential shortage areas were primarily
analyzed at the county level due in large
part to data constraints. An obvious shortcoming
of county-based analysis was that people
often cross county (or even state) lines
to seek health care. There were many counties
that had no hospitals, for example, but
their residents presumably obtained care
in other counties.
On the other hand, facilities were likely
to draw RNs from the same geographic areas
from which they draw patients, and so
a shortage of RNs in residence relative
to the estimated needs of the population
may indicate problems even if both RNs
and patients commute to an adjacent county
to give or receive care. Clearly, however,
the use of counties was inferior to the
use of service areas based on actual patterns
of health care access, but existing service
area designations were badly dated or
based on zip codes (to which the necessary
data at county or census tract levels
did not easily correspond).
Another shortcoming of counties as the
unit of analysis relates to shortages
in large metropolitan areas. In New York
City, for example, all of Manhattan is
included in a single county, but neighborhoods
within Manhattan vary widely in their
economic and demographic characteristics
and in their health care infrastructure.
Although neighborhoods with high and low
levels of resources may even be contiguous
to one another, physical and social barriers
can prevent both RNs and patients from
traveling into other neighborhoods to
give or receive care. Therefore, in the
largest metropolitan areas, we attempted
to replicate the county-level methodology
at the level of census tracts. Most of
the necessary data were available at the
census tract level.
The methodology for defining geographic
areas with shortages of RNs was inspired
in large part by the Nurse Demand Model
(NDM) and Nurse Supply Model (NSM) used
by HRSA to project nursing supply and
demand. Facilities within shortage areas
were then prioritized based upon facility
characteristics.
A. National
Models Based on County Data
There are several methods used in the
literature to estimate the demand or need
for RNs. For example, the most commonly
used measure is the ratio of RNs to population.
In addition, it is also common in the
literature to use the ratio of RNs to
MDs as a measure of the need for RNs.
In this study, we focused on the ratio
of RNs to population as a measure of the
need for RNs. When calculating this measure,
we needed to adjust population size by
the compositions of gender and age groups
in the population. The next section describes
how gender- and age-adjusted population
estimates were calculated.
The purpose of this component of the
research was to estimate relative need
of RNs across counties of the U.S. based
on RNs per gender- and age-adjusted population.
In addition, as a comparison, the ratio
of RNs to MDs was also estimated. Both
of these measures were used as a dependent
variable in an OLS regression analysis.
Data used in this study came from the
ARF of 2005 and NHIS of 2003-2004.
Assumption: RNs should be evenly distributed
across the U.S. population adjusting
for age.
Assumption: Age-specific patterns of
health care utilization do not vary
substantially across counties.
Assumption: Need for RNs (as distinct
from demand for RNs) is based on population
characteristics rather than existing
health infrastructure.
Assumption: RN commuting patterns are
similar to the commuting patterns of
other workers in terms of county inflow
and outflow.
This method was an effort to improve
upon the basic RN-to-population ratio
by applying weights to adjust for the
age distribution of the population. The
essential idea was similar to that employed
in Method 1, but was more limited in what
was accounted for to enable the same methodology
to be applied to different units of geography
so long as basic population data was available.
Because older Americans use more health
care than the general population, and
younger adults use less, this methodology
applied a greater weight to older adults
than to those ages 18-44. However, the
differences in utilization rates by age
differed by type of health care, and types
of health care differed in their demand
for RNs (e.g., about 45% of the demand
for RNs was inpatient hospital demand,
while only 6% of RN demand was nursing
home demand). The weights took into account
both estimated use of various forms of
health care and the influence of these
types of health care on the national demand
for RNs.
Table
29. National Estimates of RNs per Unit
of Service
Inpatient
units |
1,058,242 |
168,846,928 |
0.0062675 |
2,375,792 |
45% |
Outpatient
units |
145,118 |
83,715 |
1.7334767 |
2,375,792 |
6% |
Physician
offices |
278,093 |
951,214 |
0.2923559 |
2,375,792 |
12% |
Emergency
Department |
117,381 |
107,490 |
1.0920179 |
2,375,792 |
5% |
Long-term
hospitals |
139,091 |
22,402,741 |
0.0062087 |
2,375,792 |
6% |
Extended
care |
153,366 |
1,469,500 |
0.1043661 |
2,375,792 |
6% |
Home
health agency |
106,690 |
1,355,290 |
0.0787212 |
2,375,792 |
4% |
Nursing
education |
63,833 |
2,375,792 |
0.0268681 |
2,375,792 |
3% |
Public/community
health |
87,952 |
280,836,834 |
0.0003132 |
2,375,792 |
4% |
School
health |
78,539 |
49,036,764 |
0.0016016 |
2,375,792 |
3% |
Occupational
health |
22,569 |
173,907,572 |
0.0001298 |
2,375,792 |
1% |
Other |
124,918 |
280,836,834 |
0.0004448 |
2,375,792 |
5% |
Source: 2004 NSSRN
Utilization rates [1]
for specific age groups were then standardized
against the average for the population
overall to obtain ratios of how many persons
an individual from a specific age group
should count as in calculating utilization.
For example, overall use of inpatient
days in the U.S. in 2002[2]
was 541.3 per 1,000 population, while
those ages 0-5 averaged 601.0 days of
care (a ratio of 1.11). Therefore, each
person ages 0-5 would be weighted as 1.11
persons for the purposes of determining
demand for inpatient care. In contrast,
those ages 6-17 averaged 110.3 days of
inpatient care per 1,000 (a ratio of 0.20).
Therefore, each person ages 6-17 would
be weighted as 0.20 persons in determining
demand for inpatient care.
Utilization of each type of care was
further adjusted for the relative influence
of that type of care on demand for RNs
overall. For example, the greatest driver
of demand for RNs was demand for inpatient
services (where 45% of RNs are employed),
while non-emergency hospital outpatient
services influenced overall demand for
RNs much less (as they only employ 6%
of RNs). Each age group’s weight for inpatient
services was then multiplied by 0.45;
each age group’s weight for hospital outpatient
services was multiplied by 0.06, etc.
Adjusted weights for each type of care
for each group were then summed to produce
the group’s total weight, which should
be a reflection of how many people each
individual “counts as” in determining
overall RN demand. A constant adjustment
factor was then applied to the adjusted
weight for each group so that the weighted
population totals equaled the actual U.S.
population.
Final weights for each group are shown
below in Table 30. As shown, the age group
that exerted the least influence on demand
for RNs (ages 6-17) was weighted at about
half a person, while the group that exerted
the most influence (ages 85 and up) was
weighted at about five persons.
Table
30. Final Population Weights by Age Group
0
to 5 |
0.890 |
6
to 17 |
0.511 |
18
to 44 |
0.690 |
45
to 54 |
0.897 |
55
to 64 |
1.078 |
65
to 74 |
1.947 |
75
to 85 |
3.367 |
85
and up |
5.024 |
These weights were applied to the population
of each county to produce a weighted population
count that reflected demand for RNs more
accurately than simply an unweighted population
ratio.
Figure 17 below shows the projected number
of RNs per 100,000 age-adjusted population
(see Table 44 for the weights used to
adjust the population for patterns of
health care use by age).
As the figure illustrates, the supply
of RNs relative to the age-adjusted population
will peak in 2008, decline very slightly
by 2012, and decline further by 2016.
By 2024, the relative supply of RNs is
estimated to be 15% less than the 2004
level.
Figure
17. Projected RNs per 100,000 Age-Adjusted
Population
[D]
When comparing RNs per population across
counties, it is important to consider
the age-gender distribution in each county,
because this is an important determinant
of health care utilization. A county with
a higher proportion of older adults needs
more RNs compared to a county with a lower
proportion of older adults, even if the
counties have the same other characteristics.
However, numerous other factors could
affect the need for RNs. For example,
a county with higher morbidity rate needs
more RNs compared to counties with a lower
rate.
Health care utilization rate is commonly
used in adjusting population to calculate
the ratio of physicians to population.
For example, the Sheps Center for Health
Service Research, at the University of
North Carolina at Chapel Hill, has used
adjusted population to estimate physician
per population ratios. A similar procedure
was used in this study, but it focused
on RNs per population rather than physicians
per population.
Health care utilization rate was estimated
based on the number of nights in hospital
(inpatient days) and the number of visits
to health care professionals including
emergency department (outpatient visits).
The utilization rates were estimated based
on a sample of non-Hispanic Whites who
had health plans obtained from NHIS 2003-2004.
Table 31 presents the health care utilization
rates by age and gender categories for
inpatient days and outpatient visits.
Figure 18 shows the distribution of RN
hours spent in direct patient care consisting
of physician office, hospital inpatient,
and emergency department (ED), and in
non-direct patient care. These proportions
were obtained from the 2000 NSSRN. The
distribution presented in the figure was
used to aggregate inpatient days, outpatient
visits, and ED visits estimated from NHIS
data. Please note that in the NHIS dataset
physician office visits and emergency
room visits were combined into one variable.
Thus, in this study the percentage of
RN hours spent in physician office and
emergency room was consolidated into one
value, 17.6%.
Table
31. Inpatient and Outpatient Health Care
Utilization by Age and Gender, 2003-04
0
– 4 |
0.741 |
0.759 |
0.255 |
0.240 |
5
- 9 |
0.054 |
0.053 |
0.153 |
0.132 |
10
– 14 |
0.064 |
0.040 |
0.124 |
0.119 |
15
– 19 |
0.127 |
0.225 |
0.125 |
0.178 |
20
– 24 |
0.296 |
0.634 |
0.120 |
0.238 |
25
– 29 |
0.344 |
0.606 |
0.121 |
0.283 |
30
– 34 |
0.173 |
0.581 |
0.134 |
0.273 |
35
– 44 |
0.207 |
0.424 |
0.156 |
0.279 |
45
– 54 |
0.477 |
0.387 |
0.209 |
0.314 |
55
– 59 |
0.762 |
0.685 |
0.298 |
0.369 |
60
– 64 |
0.880 |
0.843 |
0.347 |
0.389 |
65
– 74 |
1.152 |
1.112 |
0.354 |
0.405 |
75
– 84 |
1.592 |
1.963 |
0.483 |
0.435 |
≥
85 |
3.567 |
2.159 |
0.512 |
0.398 |
Average |
0.746 |
0.748 |
0.242 |
0.289 |
Source: NHIS 2003-2004
Figure
18. Distribution of RN Hours Spent in
Direct Patient Care (Physician Office,
Inpatient, ER/ED) and Other Activities
[D]
Source: Calculated based on data from
the National Sample Survey of RNs, March
2000
The next step was to calculate the weight
corresponding to each age-gender group.
To illustrate, the weight for males ages
5-9 years was computed as follows:
Weight = 0.317 x (0.054/0.746) + 0.176
x (0.153/0.242) + 0.507 = 0.6410
The final weights for all age-gender
groups are presented in Table 32.
Table
32. Weights for Age-Gender Adjusted Population
0
- 4 |
1.0072 |
0.9746 |
5
- 9 |
0.6410 |
0.6098 |
10
- 14 |
0.6244 |
0.5962 |
15
- 19 |
0.6516 |
0.7106 |
20
- 24 |
0.7199 |
0.9208 |
25
- 29 |
0.7410 |
0.9356 |
30
- 34 |
0.6783 |
0.9195 |
35
- 44 |
0.7083 |
0.8563 |
45
- 54 |
0.8621 |
0.8619 |
55
- 59 |
1.0474 |
1.0216 |
60
- 64 |
1.1333 |
1.1010 |
65
- 74 |
1.2541 |
1.2248 |
75
- 84 |
1.5354 |
1.6036 |
≥
85 |
2.3960 |
1.6637 |
Note: Estimated based on NHIS 2003-2004
data and the National Sample Survey of
RNs of 2000
The final step was calculating age-gender
adjusted population using the weights
presented in Table 32. The age-gender
adjusted population for County C was calculated
as the weighted sum of populations of
all age-gender groups, formulated as follows:
Adjusted Pop = 1.0072 x (# Males 0-4)
+ … + 2.3960 x (# Males ≥ 85) +
0.9746 x (# Males 0-4) +
… +1.6637 x (# Males ≥ 85)
This method is similar to the method
commonly used to calculate the base population
to estimate the need for physicians in
a specific county, state, region, or other
geographic area.
In the first model specification, a dependent
variable defined as the ratio of RNs per
1,000 age-gender adjusted population was
generated. In addition, as a comparison,
a model with RNs per MD as the dependent
variable was also developed. The distributions
of these two dependent variables are presented
in Table A-1 to A-3 in Appendix A for
states, regions, and rural and urban areas,
respectively.
RNs per Age-Gender Adjusted Population
as the Dependent Variable
The dependent variable in the first specification
model was the ratio of RNs to age-gender
adjusted population. Explanatory variables
used in the analysis are as follows:
- Dummies for 9 census divisions with
the Pacific region used as reference
(8 dummies: dr1 - dr8)
- Dummies for metropolitan counties,
with Non-Metropolitan County used as
reference
(3 dummies: dm1 – dm3)
- Percentage of population ages 5 years
or younger(pp_5)
- Percentage of population ages 65 years
or older (pp65_)
- Percentage of Black and Hispanic population
(blck_hsp)
- Percentage of American Indian and
Alaska Native population (AIAN)
- Percentage of population in poverty
(pvrtypct)
- Infant mortality rate (infmortr)
- Percentage of agriculture/forest/fish/hunt/mine
workers (agricpct)
- Percentage of manufacturing workers
(manufpct)
- Percentage of health and social service
workers (healthpct)
- Percentage of white collar workers
(whcollar)
- Dummy for the number of hospital
in the county is more than one (dhsp2)
- MDs per 1,000 individuals (md_pop)
- Medicare inpatient days per 100 individuals
(mdicr_pop)
The descriptive statistics for each of
these variables are presented in Tables
A-4 to A-8 in Appendix A.
Table 33 presents the coefficient estimates
for the first model based on county data
from the ARF of 2005. The table shows
that the coefficient estimates of the
dummies for regions were significant and
positive. These tell us that the regions
represented by the eight dummy variables
had significantly higher RNs per age-gender
adjusted population than the Pacific region.
The coefficient estimates of the regions
varied considerably ranging from 0.515
for Mountain to 2.378 for East South Central.
The coefficient estimates of dummies for
metropolitan counties were positive and
significant indicating that counties of
metropolitan areas had higher RNs per
gender-adjusted population than non-metropolitan
counties with similar other characteristics.
Table
33. Estimates of Impact of Selected Factors
on RNs per Age-Gender Adjusted Population
Note: Estimated using OLS regression
based on data from Area Resource File
of 2005
R2 = 0.43
The coefficient estimate of proportion
of population age 5 years or younger was
positive and significant. This revealed
that the higher the proportion of population
age 5 or younger, the higher was the RNs
age-gender adjusted population. The coefficient
estimate of proportion of population age
65 or older was positive indicating that
the higher the proportion of population
age 65 years or older, the higher was
the RNs per age-gender adjusted population.
The coefficient estimate of proportion
of Black and Hispanic populations was
negative and significant indicating that
the ratio of RNs to age-gender adjusted
population was lower in counties with
higher proportion of Black and Hispanic
populations. Similar to the proportion
of Black and Hispanic populations, the
proportion of AIAN population was negatively
associated with the RNs per age-gender
adjusted population. Thus, the higher
the proportion of AIAN population in counties,
the lower was the RNs per age-gender adjusted
population.
The economic condition of a county was
represented by the percentage of population
in poverty. Its coefficient estimate was
negative and significant, which indicated
that lower the economic condition of a
county, the lower was the RNs per age-gender
adjusted population. It was noteworthy
that the economic condition had a negative
correlation with the percentage of minority
populations (Black, Hispanic, and AIAN).
Therefore, the higher the number of minority
population, especially Black, Hispanic,
and AIAN, the lower the economic condition.
The other variable which had a high negative
correlation with economic condition was
infant mortality rate. The coefficient
estimate of infant mortality rate was
negative, but not significant.
The variables representing the structure
of labor markets were percentage of agriculture/forest/
fish/hunt/mine workers, manufacturing
workers, health and social service workers,
and white collar workers. Table 33 shows
that all coefficient estimates were statistically
significant, except the coefficient estimate
for infant mortality rate. The highest
coefficient estimate of the percentage
of health and social service workers was
the highest compared to the others. This
indicated that the percentage of health
and social service workers in a county
was the most influential factor in attracting
people to enter RNs as their profession.
The number of hospitals in a county also
affected the RNs per age-gender adjusted
population. When the number of hospitals
was included in the model, the coefficient
was insignificant. In addition, the dummy
for a county having at least one hospital
was also insignificant. When a dummy variable
defined as a county with two or more hospitals
was included in the model, its coefficient
was significant and positive. A county
with one hospital and a county without
a hospital, with the same other characteristics,
tended to have the same RNs per age-gender
adjusted population. But if a county had
two or more hospitals, the number of RNs
per age-gender adjusted population was
higher compared to a county without a
hospital or with only one. This indicated
that in a county with more than one hospital,
the demand for RNs was more competitive
compared to a county without a hospital
or only one. The more competitive the
market from the demand side the higher
was the salary; in subsequence it would
attract more people to enter the nursing
profession.
The MDs per 100 individuals and Medicare
inpatient days per 100 individuals were
also included as explanatory variables.
Both variables had positive coefficient
estimates and were statistically significant.
The more MDs per individual in a county,
the greater the RNs per age-gender adjusted
population. In addition, the more Medicare
inpatient days per individual, the greater
the RNs per age-gender adjusted population.
Assumption: RNs should be evenly distributed
according to locations of physicians.
Assumption: RN commuting patterns are
similar to the commuting patterns of
other workers in terms of county inflow
and outflow.
The RN to physician ratio was expected
to produce an estimate that was closer
to that based on actual utilization data,
as physician counts were likely to be
in part a proxy for health care infrastructure.
This was more useful than utilization
rates in that it could be adapted to geographies
or time periods where utilization rates
were not available, but was a less precise
measure and could bias RN shortage estimates
against areas that were physician-short.
Example: Albany County
It was estimated that 4,942 RNs
and 1,578 physicians were working
in Albany County. If we assumed
that national RN to physician ratios
should be distributed evenly throughout
the country, we would expect 3.11
RNs to every physician in Albany
County. This would require 4,907
RNs in Albany County.
Although this method still resulted
in a slight estimated oversupply
of RNs in Albany County, this was
only 1% more RNs than needed, rather
than the 110% oversupply indicated
by Method #2. This method accounted
for the greater health care infrastructure
in Albany relative to surrounding
areas, which demanded more RNs per
capita than a simple RN to population
ratio would indicate. |
As a comparison, we also estimated a
model with RNs per MD as dependent variable.
The explanatory variables for this specification
were the same as those for the first specification
(RNs per age-gender adjusted population
as the dependent variable). Table 34 presents
the coefficient estimates for this specification.
The R2 was 0.25 for this model,
which was much lower than that of the
first specification (R2 = 0.43).
In addition, some of the coefficients
were not significant which indicated that
the specification with RNs per age-gender
adjusted population as the dependent variable
was better than the specification with
RNs per MD as the dependent variable.
It should be noted that just because
a county had high RNs per MD did not mean
the county had enough RNs. This could
be explained by a low number of MDs in
the county. For example, HPSA counties,
which had low numbers of MDs, had higher
RNs per MD. Specifically, on average,
the average RNs per MD among HPSA counties
was 16.9, which was about twice the average
of the non-HPSA and partial-HPSA counties,
at 8.9 and 8.3, respectively. In contrast,
the average RNs per age-gender adjusted
population among HPSA counties was 6.6
compared to 8.8 for non-HPSA counties
and 8.2 for partial-HPSA counties. Thus,
one must be careful when interpreting
and using RNs per MD as an indicator of
nursing shortage.
Table
34. Estimate of Impact of Selected Factors
on RNs per MD
Intercept |
18.502 |
3.901 |
4.743 |
0.000 |
New
England |
1.311 |
1.512 |
0.867 |
0.386 |
Middle
Atlantic |
1.892 |
1.215 |
1.558 |
0.119 |
East
North Central |
3.438 |
1.021 |
3.368 |
0.001 |
West
North Central |
6.760 |
0.995 |
6.797 |
0.000 |
South
Atlantic |
2.881 |
0.981 |
2.938 |
0.003 |
East
South Central |
4.100 |
1.085 |
3.779 |
0.000 |
West
South Central |
2.746 |
0.994 |
2.762 |
0.006 |
Mountain |
0.734 |
1.044 |
0.702 |
0.482 |
Counties
of metro areas of 1 million pop. or
more |
4.811 |
0.743 |
6.478 |
0.000 |
Counties
in metro areas of 250,000 - 1,000,000
pop. |
4.302 |
0.689 |
6.242 |
0.000 |
Counties
in metro areas of fewer than 250,000
pop. |
4.557 |
0.644 |
7.077 |
0.000 |
Percentage
of population age 5 years or younger |
-0.661 |
0.266 |
-2.485 |
0.013 |
Percentage
of population age 65 years or older |
0.059 |
0.069 |
0.854 |
0.393 |
Percentage
of Black and Hispanic population |
-0.012 |
0.016 |
-0.754 |
0.451 |
Percentage
of AIAN population |
-0.015 |
0.032 |
-0.472 |
0.637 |
Percentage
of population in poverty |
-0.085 |
0.061 |
-1.391 |
0.164 |
Infant
mortality rate |
-0.003 |
0.056 |
-0.060 |
0.952 |
Percentage
of agriculture/forest/fish/hunt/mine
workers |
0.182 |
0.043 |
4.256 |
0.000 |
Percentage
of manufacturing workers |
0.079 |
0.035 |
2.221 |
0.026 |
Percentage
of health and social service workers |
0.252 |
0.058 |
4.322 |
0.000 |
Percentage
of white collar workers |
-0.226 |
0.046 |
-4.911 |
0.000 |
Dummy
for county having 2 or more hospitals |
-3.006 |
0.463 |
-6.494 |
0.000 |
Number
of MDs per 1,000 individuals |
-2.064 |
0.172 |
-12.009 |
0.000 |
Medicare
inpatient days per 100 individuals |
-1.630 |
0.569 |
-2.862 |
0.004 |
Note: Estimated using OLS regression
based on data from Area Resource File
of 2005
R2 = 0.25
Table 35 presents the distribution of
the percentage of counties with negative
residual by states. (The residual was
defined as the actual value of the dependent
variable less its predicted value, so
that a negative value indicated that a
state has fewer RNs than the model predicts.)
Based on the first specification, the
table shows that—apart from District of
Columbia—Utah had the highest percentage
of counties with negative residual (83%).
In the other words, 83% of counties in
Utah had lower RNs per age-gender adjusted
population than predicted by the model.
In contrast, Hawaii and Montana had the
lowest percentage of counties with negative
residuals (25% each).
Table
35. Percentages of Counties in the U.S.
with Negative Residuals
1 |
Alabama |
52% |
63% |
2 |
Alaska |
59% |
33% |
4 |
Arizona |
53% |
40% |
5 |
Arkansas |
33% |
66% |
6 |
California |
57% |
54% |
8 |
Colorado |
38% |
49% |
9 |
Connecticut |
63% |
13% |
10 |
Delaware |
33% |
33% |
11 |
District
of Columbia |
100% |
0% |
12 |
Florida |
63% |
73% |
13 |
Georgia |
50% |
67% |
15 |
Hawaii |
25% |
0% |
16 |
Idaho |
73% |
61% |
17 |
Illinois |
30% |
49% |
18 |
Indiana |
58% |
79% |
19 |
Iowa |
36% |
48% |
20 |
Kansas |
44% |
69% |
21 |
Kentucky |
57% |
70% |
22 |
Louisiana |
44% |
55% |
23 |
Maine |
56% |
81% |
24 |
Maryland |
38% |
50% |
25 |
Massachusetts |
36% |
50% |
26 |
Michigan |
75% |
79% |
27 |
Minnesota |
72% |
87% |
28 |
Mississippi |
28% |
52% |
29 |
Missouri |
67% |
59% |
30 |
Montana |
25% |
60% |
31 |
Nebraska |
58% |
75% |
32 |
Nevada |
77% |
67% |
33 |
New
Hampshire |
50% |
50% |
34 |
New
Jersey |
67% |
43% |
35 |
New
Mexico |
42% |
53% |
36 |
New
York |
55% |
50% |
37 |
North
Carolina |
31% |
65% |
38 |
North
Dakota |
62% |
81% |
39 |
Ohio |
46% |
78% |
40 |
Oklahoma |
57% |
45% |
41 |
Oregon |
56% |
65% |
42 |
Pennsylvania |
45% |
67% |
44 |
Rhode
Island |
40% |
80% |
45 |
South
Carolina |
44% |
83% |
46 |
South
Dakota |
41% |
89% |
47 |
Tennessee |
70% |
79% |
48 |
Texas |
62% |
70% |
49 |
Utah |
83% |
62% |
50 |
Vermont |
64% |
71% |
51 |
Virginia |
55% |
59% |
53 |
Washington |
46% |
46% |
54 |
West
Virginia |
60% |
60% |
55 |
Wisconsin |
71% |
86% |
56 |
Wyoming |
52% |
74% |
Figure
19. Estimated Extent of Nursing Shortages
in Counties in the U.S.
[D]
B. Model Based
on RN to Population Ratios
Assumption: RNs should be evenly distributed
across the U.S. population.
Assumption: Need for RNs (as distinct
from demand for RNs) is based on population
characteristics rather than existing
health infrastructure.
Assumption: RN commuting patterns are
similar to the commuting patterns of
other workers in terms of county inflow
and outflow.
Method 2 uses a simple, RN-to-population
ratio and is based upon the assumption
that RNs should be evenly distributed
across the U.S. population. Method 2 is
a very crude measure because it does not
take into account either the age structure
of the population at the county level
or the health care infrastructure in the
county. Like Method 1, it adjusts RN supply
based on inter-county commuting patterns.
Example: Albany County,
New York
As calculated in Step 4 of Methodology
#1, 4,942 RNs were estimated to
work in Albany County in 2000. The
population of Albany County in 2000
was estimated to be 294,565. Applying
national ratios of 0.0080 RNs per
population, we would expect Albany
County to need a total of 2,357
RNs (294,565 x 0.0080). The actual
supply of RNs was estimated to be
110% more than what the population
of the county required.
Albany County is a good illustration
of the shortcomings of this method.
Because it is an urban center with
many hospitals and other health
care facilities, many residents
of surrounding counties come to
Albany County for care. Even though
there are facilities in most of
the surrounding counties, Albany
Medical Center is a Level I trauma
center and a teaching hospital,
and both Albany Medical Center and
St. Peter’s Hospital (also in Albany)
score highly on national rankings
of patient care. |
C. Models
Based on County Clusters
One of the obvious biases when Methods
1 to 4 were compared was that a county
in which health care facilities drew many
patients from outside the county, the
county was shown to have more severe shortages
than counties in which patients presumably
traveled to other counties for health
care. This was a clear problem in any
methodology based solely on population.
In an attempt to assess the impact of
cross-county patient flow, Methods 1 to
4 were recalculated at the level of “county
clusters,” where population counts, nurse
counts, and demand estimates at the county
level were summed for a core county and
its contiguous counties. This was an imperfect
measure, as contiguous counties will have
a patient flow to and from the core county
in the cluster, but also to and from their
own other contiguous counties. For example,
if County A has a contiguous County B
to the west, County B’s population is
considered part of County A’s county cluster.
However, if County B is bordered on the
west by County C, which is part of a major
metropolitan area, County B’s population
may be primarily going to County C for
health care with very little flow to County
A. Counting the population of County B
as part of County A’s county cluster will
therefore result in an overestimate of
the pool of people who may be using health
services in County A.
On the other hand, the use of county
clusters was expected to have a smoothing
effect across the various types of estimates,
which was generally observed. For example,
in Albany County, estimates of RN supply
ranged from a supply that was 1% greater
than demand to a supply that was 110%
greater than demand. In the Albany county
cluster, however, estimates ranged from
a supply that was a 3% shortage to a supply
that was 39% more than estimated demand.
Example: Albany and Schoharie
Counties in Upstate New York
Figure 20 below summarizes how
the various measures of shortage
differ for a feeder county and a
receiver county that are contiguous
to one another. Schoharie County
was a rural county adjacent to Albany
County. None of its other contiguous
counties hosted major medical centers
comparable to those in Albany County,
so persons in Schoharie County were
more likely to go to Albany County
than to any other contiguous county
for care. In the first four measures
of shortage, at the individual county
level, Albany County was seen as
having a surplus while Schoharie
County was seen as having a shortage.
When county clusters were used,
however, estimates for the two neighboring
counties were much more similar.
Figure
20. Comparison of Selected Measures
of Nursing Shortage in Adjacent
Counties
[D] |
D. Models
Based on Adjusting for Cross-County Patient
Flow
Another method to adjust for cross-county
patient flow more precisely than using
county clusters was to adjust population
figures based upon commuting flow. In
one respect it made sense that the distances
and directions in which it was convenient
for people to travel to work would also
be convenient for them to travel for health
care, and that counties with more job
opportunities relative to their neighbors
would also have more health care facilities.
On the other hand, the nature of health
care need dictates that some areas may
have health facilities but few other major
employers.
Furthermore, there were sometimes additional
inducements to commute out for work that
did not exist in commuting for health
care. For example, in Monroe County, Pennsylvania,
7% of workers who lived in the county
commuted to one of the five counties of
New York City for work (a distance of
approximately 80 miles that cannot be
traveled without crossing through at least
three other counties) due to the great
differences in salaries (favoring working
in New York City) and the great differences
in cost of living (favoring living in
Pennsylvania). Yet Monroe County has a
medical center, and is contiguous to several
other counties with major medical centers
(including some with trauma centers) that
are not nearly as far as New York City.
Therefore, it was doubtful that 7% of
the population of Monroe County traveled
to New York City for health care, despite
commuting patterns for work. Areas with
such extreme commuting patterns to counties
that were not contiguous were certainly
the exception rather than the rule, but
may be more common than believed, especially
near major metropolitan areas with very
high costs of living.
Adjustments for patient flow were similar
to those made to RN supply. This produced
the same RN-to-population ratio as using
the unadjusted RN numbers and unadjusted
population together, but produced different
raw estimates.
Using this methodology, we found that
Albany County, New York was estimated
to need 3,634 RNs to treat its own population
and incoming patients from other areas,
while 4,942 RNs were estimated to work
there. This estimated a supply that exceeded
demand by 36%, which was a more moderate
oversupply estimate than most others using
Albany County as a single county, and
somewhat comparable to those using the
county cluster.
Schoharie County, using this methodology,
was estimated to need 191 RNs, and had
197 (a shortage of 3%). This also appeared
to be a moderate number compared to other
estimates.
Monroe County, Pennsylvania was found
to require 922 RNs, and had an estimated
834 working (a shortage of 9.5%). It was
not surprising that this was lower than
the other shortage estimates based on
population ratios (25% and 23%), but it
was surprising that it was so close
to estimates based on actual health care
use (11%).
New York County was found to need 34,126
RNs while there were an estimated 22,711
working there. This shortage (33%) was
also very close to that based on actual
health care use (29%).
Except for Albany County, adjustments
of both population and RN supply based
on commuting patterns to produce a ratio
seemed to offer close approximations of
estimates based on actual health care
use in each of the test counties, including
New York County and Monroe County, Pennsylvania,
both of which experienced unusual levels
of commuter flow.
E. Models
Based on Sub-County Analyses
As the work on the county-level analyses
described above progressed, concerns arose
that counties were too large to study
and understand the nursing needs of communities
in the largest metropolitan areas, where
very disadvantaged communities may exist
in close proximity to very advantaged
communities. Disadvantaged communities
in urban areas may have a more difficult
time recruiting RNs for two reasons:
- RNs may be reluctant to work in communities
where there is a perceived fear of crime
or a large population with which they
do not feel culturally competent, and
- a large percentage of the services
offered in disadvantaged urban communities
are provided by publicly operated facilities,
which may not be able to offer salaries
and benefits competitive with nearby
non-public facilities that tend to serve
more advantaged communities.
For this reason, some sub-county analyses
were performed at the Census tract level
using New York County (Manhattan) as a
test case. These analyses were largely
exploratory in nature, to try to determine
what data might be available and what
methods might be appropriate for sub-county
analyses in the largest metropolitan areas
across the U.S.
Census tract-level analyses posed many
challenges. Demographic and economic population
data were available at the Census tract
level, and some RN supply data was available
from the 2000 Census as well. Utilization
data, however, was not available, nor
were data on commuting patterns between
Census tracts. There was also a question
of the accuracy of RN supply estimates
from sample data at such a small level
of analysis. Ultimately, utilization rates
were imputed based on the demographic
characteristics of the tract population,
the utilization data for the county, and
the distribution of the county population
between Census tracts.
It became very clear, however, that RNs
in the population were not an adequate
measure of available supply at the Census
tract level. Some of the poorer neighborhoods
had relatively high numbers of RNs per
capita, but there was no basis for estimating
how many of them worked in the neighborhoods
where they lived. Similarly, many wealthy
neighborhoods had relatively few RNs per
capita (who presumably would not be able
to afford to live in the most expensive
neighborhoods of Manhattan), but there
was no basis for assuming that the residents
of these neighborhoods necessarily had
difficulty obtaining nursing care. Estimates
of service use in the population were
also deemed suspect because it was impossible
to estimate how many residents obtained
health care within their own Census tract.
Subsequent reflection on the nature of
labor markets and discussions with providers
in New York County led study staff to
believe that RN supply was not necessarily
a correlate of difficulty recruiting at
the local level. In large metropolitan
areas, the pool of available labor tended
to be geographically very broad, as illustrated
by the fact that 70% of workers employed
in New York County did not reside in New
York County and that 16% of the employed
residents of New York County did not work
in New York County. It would thus appear
that the supply of RNs within the Census
tract where a health care facility was
located was of limited relevance to the
overall supply of RNs from which that
facility may draw. Factors such as the
ability to offer competitive compensation
packages and the perceived environment
of the neighborhood were likely to be
much greater predictors of difficulty
recruiting RNs in large metropolitan counties.
It may be best to establish guidelines
specific to facilities in the largest
metropolitan counties that would address
the specialized problems of high-needs
facilities. Possibilities include giving
automatic eligibility to facilities in
HPSA-designated areas or to those meeting
certain criteria (e.g., public facilities),
regardless of the eligibility of the overall
county. To implement such a policy, it
would be necessary to define a threshold
for counties in which these automatic
qualifications would apply (perhaps counties
with populations of more than 1 million).
F. Factor
Analysis of Nursing Shortage Indicators
The purpose of the factor analysis was
to construct a smaller number of underlying
common factors that could explain a large
number of observed variables. This analysis
was performed primarily mainly due to
the lack of a single independent variable
that could be used to measure nursing
shortage that was comparable for all U.S.
counties. The data used in this analysis
came from the ARF 2005 release. In this
analysis we chose three factors to describe
the characteristics of counties in the
U.S. based on 20 observed variables. The
three factors explained 50.3% of total
variation of the observed variables.
The list of the observed variables and
the corresponding standardized scoring
coefficients for each factor are presented
in Table 36. Shadowed numbers were the
highest coefficient for the corresponding
variable, which revealed what variables
were the primary bases for each factor.
Note that 21.54% of U.S. counties were
excluded from the analysis, mainly due
to missing values or no hospital in those
counties. Also, counties without hospitals
were excluded from the analysis because
a hospital was an important factor in
analyzing nursing shortages. Most RNs
were employed in hospital settings, which
implied that hospitals drive the market
for RNs. The counties without a hospital
could be analyzed separately, but this
had not been done at the time of this
writing.
Table
36. Standardized Scoring Coefficients
Metropolitan
dummy |
-0.025 |
-0.044 |
0.188 |
RNs
per 1,000 individuals |
0.003 |
0.256 |
0.012 |
RNs
per 1,000 individuals < 5 years |
-0.007 |
0.259 |
0.002 |
RNs
per 1,000 individuals >=65 years |
0.005 |
0.109 |
0.122 |
RNs
per hospital bed |
0.213 |
-0.052 |
0.048 |
RNs
per MD |
0.136 |
0.096 |
-0.132 |
RNs
per 1,000 civilian labor force |
0.020 |
0.274 |
-0.045 |
RNs
per 1,000 inpatient days |
0.272 |
-0.059 |
-0.058 |
RNs
per 1,000 outpatient visits |
0.158 |
0.007 |
-0.016 |
RNs
per 1,000 emergency room visits |
0.134 |
0.066 |
0.016 |
Infant
mortality rate |
0.028 |
0.019 |
-0.140 |
RNs
per 100 Medicare inpatient days |
0.278 |
-0.053 |
-0.038 |
RNs
per 100 Medicaid inpatient days |
0.220 |
-0.018 |
-0.069 |
Median
household income ($10,000) |
-0.027 |
-0.091 |
0.310 |
Percent
persons in poverty |
0.037 |
0.052 |
-0.297 |
Unemployment
rate |
0.064 |
-0.037 |
-0.151 |
Percentage
of manufacturing workers |
0.057 |
-0.102 |
0.036 |
Percentage
of health service workers |
-0.041 |
0.232 |
-0.168 |
Percentage
of Blacks and Hispanics |
0.010 |
-0.053 |
-0.098 |
Percentage
of AIAN |
0.020 |
0.061 |
-0.119 |
Note: The three factors can explain 50.3
percent of total variation of all variables
The standardized scoring coefficients
suggested that Factor 1 consisted of high
positive loadings on RNs per hospital
bed, RNs per MD, RNs per 1,000 inpatient
days, RNs per 1,000 outpatient visits,
RNs per 1,000 emergency room visits, RNs
per 100 Medicare inpatient days, and RNs
per 100 Medicaid inpatient days. These
loadings indicated that Factor 1 represented
the ratio of RNs to health care utilization,
especially in hospitals. A county with
a high value for Factor 1 indicated that
the county had a high number of RNs relative
to health care utilization compared to
other counties. On the other hand, a county
with a low value for Factor 1 indicated
that the county faced a nursing shortage
problem, especially a shortage related
to health care utilization in hospitals.
Note that a county might score high on
Factor 1 just because the county has low
health care utilization due to underdeveloped
health care infrastructure. Conversely,
a county might score low on Factor 1 just
because the county has high number of
health care facilities which attracts
many people from other counties for health
care services. So, one must be cautious
when interpreting Factor 1, and in particular,
it should be interpreted in the context
of the other two factors.
Factor 2 consisted of high positive loadings
on RNs per 1,000 population, RNs per 1,000
individuals younger than 5 years, RNs
per 1,000 individuals age 65 years or
older, RNs per 1,000 civilian labor forces,
and the percentage of health service workers;
and a high negative loading on the percentage
of manufacturing workers. These patterns
suggested that Factor 2 represented the
ratio of RNs to age-adjusted population.
In addition, this factor also represented
the supply of RNs. The lower the percentage
of the manufacturing workers in a county,
the more likely an individual was to enter
the health care industry, including nursing
profession. A county with high value for
Factor 2 would generally have more RNs
per capita than other counties. This factor
was clearer in describing the nursing
shortage than was Factor 1.
Factor 3 consisted of high positive loadings
on the metropolitan dummy variable, RNs
per individuals age 65 years and older,
median household income (x $10,000); and
high negative loadings on RNs per MD,
infant mortality rate, unemployment rate,
the percentage of individuals in poverty,
the percentage of Black and Hispanic populations,
and the percentage of American Indian
and Alaska Native population. These patterns
suggested that Factor 3 represented the
economic condition of a county, including
the percentage of minority populations.
The percentage of minority population
and quality of health were highly correlated
with economic condition. A county with
a high value for Factor 3 indicated that
the county was in a metropolitan area
with good economic conditions and lower
percentage of minority populations compared
to other counties.
The three factors above can be combined
to describe a nursing shortage condition
of each county in the U.S. To illustrate
how this might work, suppose we divide
each of the factors into two categories
based on its median: lower than median
and higher than median. (The threshold
is arbitrarily chosen and could be replaced
with other values, e.g., using the first
quartile or other statistics.) Based on
the three factors, each divided into two
categories, all counties in the U.S. can
be grouped into eight categories. Note
that it was very common that nursing shortage
was measured using the ratio of RNs to
population (or age-adjusted population).
As described before, Factor 2 represented
the ratio of RNs to population. Based
on this common criterion, Factor 2 was
considered to be the most obvious factor
in characterizing nursing shortage condition.
So the categories were constructed based
on the combinations of Factor 2, Factor
1, and Factor 3 which resulted in 8 categories:
“111,” “112,” “121,” “122,” “211,” “212,”
“221,” and “222”. The interpretations
of these categories are described as follows.
- Category 111. Counties in
this category had low values of the
three factors. Intuitively, they were
counties with a low number of RNs relative
to population, low number of RNs relative
to health care utilizations, and low
economic conditions including a high
proportion of minority populations and
low quality of health. In general, counties
in this category were counties with
a nursing shortage problem and low economic
conditions, so they needed to be supported
by the government to increase the number
of RNs in those counties.
- Category 112. Counties in
this category had a low number of RNs
relative to population, low number of
RNs relative to health care utilization,
and good economic conditions. Also,
they were counties with a rich population.
In addition, the health care industry
in these counties was less attractive
compared to other industries, suggesting
not many people in these counties were
interested in entering the nursing profession.
- Category 121. Counties in
this category had a low number of RNs
relative to population, high number
of RNs relative to health care utilization,
and low economic conditions. The high
number RNs relative to health care utilization
may have been due to the small number
of health care infrastructures (e.g.,
one hospital). People from these counties
may have gone to other counties for
health care services because the amount
of health care utilization in those
counties was low. In subsequent, the
ratio of RNs to health care utilization
was high. Therefore, the high value
of Factor 2 was not necessarily because
of a high number of RNs but probably
because of the limited health care infrastructure.
- Category 122. Counties in
this category had a low number of RNs
relative to population, high number
of RNs relative to health care utilization,
and good economic conditions. The high
number of RNs relative to health care
utilization may have been due to the
low number of health care infrastructures,
therefore people in these counties went
to other counties for health care services.
These counties were similar to those
in category 121, except for the economic
conditions.
- Category 211. Counties in
this category had a high number of RNs
relative to population, low number RNs
relative to health care utilization,
and low economic conditions. One possible
reason for the low number of RNs relative
to health care utilization was a highly
developed health care infrastructure,
therefore people from other counties
came to these counties for health care
services.
- Category 212. Counties in
this category had a high number of RNs
relative to population, low number of
RNs relative to health care utilization,
and good economic conditions. Counties
in this category were similar to counties
in category 211, except for the economic
condition. They may not have had a nursing
shortage problem because people from
other counties came to these counties
for health care utilization which suggested
a low ratio of RNs to health care utilization.
In addition, the counties in this category
did not have economic problems.
- Category 221. Counties in
this category had a high number of RNs
relative to population, high number
of RNs relative to health care utilization,
and low economic conditions. These counties
did not have nursing shortage problems,
but had economic problems which included
a high proportion of minority populations
and a low quality of health.
- Category 222. Counties in
this category had a high number of RNs
relative to population, high number
of RNs relative to health care utilization,
and good economic conditions. These
counties did not have nursing shortage
problems and were without economic problems.
Now let us look at the distribution of
counties by the categories for each Census
division region as presented in Table
37. Among the nine regions, West South
Central had the highest percentage of
counties in category “111,” which was
22% of the counties in the region. The
second highest was Mountain (18%), followed
by South Atlantic (11%), and East South
Central (10%). On the other hand, New
England had the highest percentage of
counties in category “222,” which was
39% of counties in the region. Those counties
did not have nursing shortage problem
and had good economic conditions. The
second highest was Middle Atlantic (19%),
followed by East North Central (17%),
and West North Central (13%).
Table
37. Distribution of Counties by Categories
for each Census Division
East
North Central |
70 |
14 |
40 |
23 |
50 |
20 |
111 |
34 |
75 |
437 |
16.0% |
3.2% |
9.2% |
5.3% |
11.4% |
4.6% |
25.4% |
7.8% |
17.2% |
100% |
East
South Central |
80 |
38 |
22 |
32 |
20 |
71 |
35 |
46 |
20 |
364 |
22.0% |
10.4% |
6.0% |
8.8% |
5.5% |
19.5% |
9.6% |
12.6% |
5.5% |
100% |
Middle
Atlantic |
13 |
3 |
13 |
26 |
48 |
2 |
8 |
9 |
28 |
150 |
8.7% |
2.0% |
8.7% |
17.3% |
32.0% |
1.3% |
5.3% |
6.0% |
18.7% |
100% |
Mountain |
67 |
49 |
55 |
29 |
18 |
13 |
22 |
22 |
5 |
280 |
23.9% |
17.5% |
19.6% |
10.4% |
6.4% |
4.6% |
7.9% |
7.9% |
1.8% |
100% |
New
England |
4 |
1 |
2 |
2 |
25 |
1 |
2 |
4 |
26 |
67 |
6.0% |
1.5% |
3.0% |
3.0% |
37.3% |
1.5% |
3.0% |
6.0% |
38.8% |
100% |
Pacific |
25 |
20 |
30 |
10 |
12 |
18 |
29 |
15 |
5 |
164 |
15.2% |
12.2% |
18.3% |
6.1% |
7.3% |
11.0% |
17.7% |
9.2% |
3.0% |
100% |
South Atlantic |
167 |
66 |
58 |
55 |
41 |
66 |
56 |
44 |
36 |
589 |
28.4% |
11.2% |
9.8% |
9.3% |
7.0% |
11.2% |
9.5% |
7.5% |
6.1% |
100% |
West
North Central |
147 |
34 |
50 |
62 |
89 |
21 |
35 |
97 |
83 |
618 |
23.8% |
5.5% |
8.1% |
10.0% |
14.4% |
3.4% |
5.7% |
15.7% |
13.4% |
100% |
West South Central |
103 |
102 |
22 |
52 |
18 |
72 |
30 |
58 |
12 |
469 |
22.0% |
21.8% |
4.7% |
11.1% |
3.8% |
15.4% |
6.4% |
12.4% |
2.6% |
100% |
Total |
676 |
327 |
292 |
291 |
321 |
284 |
328 |
329 |
290 |
3138 |
21.5% |
10.4% |
9.3% |
9.3% |
10.2% |
9.0% |
10.4% |
10.5% |
9.2% |
100% |
Note: (a) An example of how
to interpret the category: 121 means F1<median,
F2>median, F3<median
Table 38 presents the distribution of
counties by the categories for each rural/urban
code. More than 50% of counties in the
completely rural areas (Codes 8 and 9)
had missing values or did not have a hospital.
Apart from the two areas (8 and 9), the
higher the codes (more rural the county),
the higher was the percentage of counties
in category “111.” Less than 5% of counties
in metro areas were categorized as “111.”
In contrast, more than 14% of counties
in the non-metro areas were categorized
as “111.” On the other hand, the percentage
of counties categorized as “222” was lower
as the code increased (more rural the
county). Twenty-two percent of counties
of metro areas of 1 million population
or more (Code=1) were categorized as “222.”
In contrast, only 2.5% of counties of
completely rural areas were categorized
as “222.”
Table
38. Distribution of Counties by Categories
for each Rural/Urban Code
1 |
72 |
17 |
72 |
3 |
47 |
8 |
100 |
2 |
92 |
413 |
17.4% |
4.1% |
17.4% |
0.7% |
11.4% |
1.9% |
24.2% |
0.5% |
22.3% |
100% |
2 |
63 |
10 |
37 |
11 |
72 |
14 |
52 |
10 |
56 |
325 |
19.4% |
3.1% |
11.4% |
3.4% |
22.2% |
4.3% |
16.0% |
3.1% |
17.2% |
100% |
3 |
82 |
13 |
46 |
33 |
85 |
14 |
30 |
13 |
35 |
351 |
23.4% |
3.7% |
13.1% |
9.4% |
24.2% |
4.0% |
8.6% |
3.7% |
10.0% |
100% |
4 |
3 |
32 |
18 |
38 |
15 |
28 |
41 |
23 |
20 |
218 |
1.4% |
14.7% |
8.3% |
17.4% |
6.9% |
12.8% |
18.8% |
10.6% |
9.2% |
100% |
5 |
1 |
17 |
12 |
26 |
18 |
8 |
10 |
7 |
6 |
105 |
1.0% |
16.2% |
11.4% |
24.8% |
17.1% |
7.6% |
9.5% |
6.7% |
5.7% |
100% |
6 |
73 |
101 |
30 |
56 |
20 |
123 |
68 |
94 |
43 |
608 |
12.0% |
16.6% |
4.9% |
9.2% |
3.3% |
20.2% |
11.2% |
15.5% |
7.1% |
100% |
7 |
39 |
84 |
52 |
71 |
36 |
48 |
21 |
78 |
21 |
450 |
8.7% |
18.7% |
11.6% |
15.8% |
8.0% |
10.7% |
4.7% |
17.3% |
4.7% |
100% |
8 |
124 |
18 |
7 |
13 |
5 |
23 |
4 |
35 |
6 |
235 |
52.8% |
7.7% |
3.0% |
5.5% |
2.1% |
9.8% |
1.7% |
14.9% |
2.6% |
100% |
9 |
219 |
35 |
18 |
40 |
23 |
18 |
2 |
67 |
11 |
433 |
50.6% |
8.1% |
4.2% |
9.2% |
5.3% |
4.2% |
0.5% |
15.5% |
2.5% |
100% |
Total |
676 |
327 |
292 |
291 |
321 |
284 |
328 |
329 |
290 |
3138 |
21.5% |
10.4% |
9.3% |
9.3% |
10.2% |
9.0% |
10.5% |
10.5% |
9.2% |
100% |
Notes:
(b)
- Counties
of metro areas of 1 million population
or more
- Counties
in metro areas of 250,000 ‑ 1,000,000
population
- Counties
in metro areas of fewer than 250,000
population
- Urban
population of 20,000 or more, adjacent
to a metro area
- Urban
population of 20,000 or more, not adjacent
to a metro area
- Urban
population of 2,500‑19,999, adjacent
to a metro area
- Urban
population of 2,500‑19,999, not
adjacent to a metro area
- Completely
rural or less than 2,500 urban population,
adjacent to a metro area
- Completely
rural or less than 2,500 urban population,
not adjacent to a metro area
Table 39 presents the distribution of
counties by the categories for each HPSA
designation code. Almost 50% of whole-HPSA
counties had missing values or did not
have a hospital. The percentage of counties
categorized as “111” was almost equal
in non-HPSA counties and whole-HPSA counties,
at 9% each. On the other hand, 14% of
non-HPSA counties were categorized as
“222,” in contrast to 3% of whole-HPSA
counties, and 10% of partial-HPSA counties.
Table
39. Distribution of Counties by Categories
for each HPSA Code (Primary Care)
None |
105 |
74 |
78 |
84 |
93 |
44 |
129 |
85 |
110 |
802 |
13.1% |
9.2% |
9.7% |
10.5% |
11.6% |
5.5% |
16.1% |
10.6% |
13.7% |
100% |
Whole
County |
392 |
74 |
31 |
43 |
4 |
101 |
37 |
100 |
23 |
805 |
48.7% |
9.2% |
3.8% |
5.3% |
0.5% |
12.6% |
4.6% |
12.4% |
2.9% |
100% |
Part
County |
179 |
179 |
183 |
164 |
224 |
139 |
162 |
144 |
157 |
1531 |
11.7% |
11.7% |
12.0% |
10.7% |
14.6% |
9.1% |
10.6% |
9.4% |
10.2% |
100% |
Total |
676 |
327 |
292 |
291 |
321 |
284 |
328 |
329 |
290 |
3138 |
21.5% |
10.4% |
9.3% |
9.3% |
10.2% |
9.0% |
10.4% |
10.5% |
9.2% |
100% |
[1]
Taken from Health, United States, 2005
[2] Data
were not published for 2000.
|