A. Study Background
and Context
In 2004, the Health Services and Resources
Administration (HRSA) issued a Request
for Proposals for a two-year research
project to gather information and insights
in support of the development of a new
methodology for identifying health care
facilities and communities with critical
shortages of registered nurses (RNs).
HRSA’s decision to support this research
was based in large part on their concern
that its current method for identifying
facilities and communities with shortages
of RNs was too narrow in scope and that
RN shortages were likely to worsen over
the next 20 years, The New York Center
for Health Workforce Studies at SUNY Albany
was selected to conduct this study.
This report summarizes the findings of
the various components of this empirical
research study. It describes a number
of methods for identifying facilities
and communities with shortages of RNs.
It documents the strengths and weaknesses
of different methods for assessing the
extent of shortages of RNs in facilities
and communities. The report is presented
in six sections, each summarizing a different
aspect of the study.
- Study Background and Context
- Methods, Models, and Analyses Using
Facility Data
- Methods, Models, and Analyses Using
Only Geographic Data
- Preferred Method
- Additional Analyses and Explorations
- Conclusions and Recommendations
The conclusions are designed to inform
policy analysts and other researchers
who may be interested in implementing
or adapting one or more of these methods
in the future. Additional details about
the different methods, including preliminary
estimates of the supply and demand for
RNs in counties and other jurisdictions,
can also be found in the report.
The Federal government has had a long-standing
interest in the nursing workforce. For
more than two decades, through its National
Center for Health Workforce Analysis,
Division of Nursing and the Shortage Designation
Branch of HRSA has collected data on RNs
in the U.S. and developed quantitative
models to estimate the current and future
supply of and demand for RNs. Several
programs to encourage new RNs to practice
in facilities and communities with severe
shortages of RNs, including the Nursing
Education Loan Repayment Program (NELRP)
and the Nursing Scholarship Program, have
been operating for many years. These programs
help to alleviate persistent shortages
of RNs.
In framing the parameters for this research
study, HRSA identified a number of issues
that needed resolution including:
- Should indicators developed to
measure critical shortages of RNs
be based on need for RNs or
demand for RNs?
- Can a standard set of indicators
of critical shortages of RNs be developed
and applied to all of the eligible
settings included in this study?
- Can variations in the supply of
and demand for RNs by region, geography
(i.e., rural or urban), setting, or
facility be accounted for in indicators
that measure RN shortages?
- Are setting-specific data sets
available at the national level that
include the elements needed to measure
critical shortages of RNs?
- Can a process be developed that
identifies facilities with the most
serious shortages of RNs so that Federal
resources can be targeted on the neediest
facilities?
- How can true shortages of RNs at
a facility be distinguished from shortages
created by poor management practices?
An effective study must take all of these
issues into account while researching
and evaluating new methods to measure
shortages of RNs. Ideally, a new method
can be developed to support government
programs that encourage new RNs to practice
in facilities and communities with severe
shortages. Such a method would also provide
a better basis for monitoring RN shortages
locally and nationally.
One important Federal response to the
national nursing shortage was the Nurse
Reinvestment Act, which was enacted in
August 2002. The Act reauthorized the
NELRP, which provides loan repayment to
RNs in return for work at facilities or
in communities with a shortage of RNs,
and established the Nursing Scholarship
Program. Eligible placement sites for
these programs were expanded to include:
- Ambulatory surgical centers;
- Federally designated migrant, community
public housing, or homeless health centers;
- Federally qualified health centers;
- Home health agencies;
- Hospice programs;
- Hospitals;
- Indian Health Service centers;
- Native Hawaiian health centers;
- Nursing homes;
- Rural health clinics; and
- State or local health department
clinics or skilled nursing facilities.
The method used for the identification
of qualified placement sites included
a combination of geographic and facility
designations. In 2002, the New York Center
for Health Workforce Studies assisted
the Bureau of Health Professions by developing
an up-to-date list of nursing shortage
hospitals and counties throughout the
United States and its territories. The
Center used two separate methodologies,
one to identify private, non-profit hospitals
with shortages of RNs and the second to
identify counties with shortages of RNs.
Because this approach relied on hospital
nursing data to identify facilities with
nursing shortages, it failed to quantify
nursing shortages experienced by any providers
except hospitals. Most of the other types
of facilities included on the list above
were considered categorically eligible,
based on the premise that they faced critical
shortage of RNs.
In the general context described above,
this study was conducted over a two-year
period, starting in the fall of 2004.
After a brief summary of the study goals,
objectives, and other characteristics
of the study, the ten study components
are summarized below.
a. Project Goals
and Objectives
The primary goal of this study was to
conduct research on the necessary components
of a comprehensive, nationwide methodology
to identify facilities and communities
with critical shortages of RNs across
the U.S. and its territories in order
to target the placement of Federally-obligated
RN scholars and loan repayers. This research,
which involved statistical analysis supported
by expert opinion, took into account population
needs, practice settings, appropriate
staffing levels, and nursing education,
among other aspects of the supply of and
demand for RNs. As a secondary benefit,
the project revealed important insights
about the differences in the use and distribution
of RNs across the various settings and
geographic areas of the country.
The study’s staff worked to achieve the
following objectives in support of the
primary goal of the study:
- Identify and define indicators and
measures that reflect critical RN shortages
for the four types of facilities;
- Assess the availability of data sets
that can be used to determine RN staffing
needs nationally in each of the settings
listed above;
- Develop quantifiable key measures
of nursing shortages based on key indicators
described above as well as the available
data sets that include the necessary
data to calculate the key measure.
- Determine whether these key measures
of shortage can be incorporated into
a comprehensive national methodology
to identify facilities and agencies
with critical nursing shortages based
on the following criteria:
- the measure accurately quantifies
nursing shortages in a specific
health care setting; and
- the measure either can be calculated
using an available national data
set or the data can be collected
and validated at the facility level.
- Establish an analytic framework that
can be used for a comprehensive methodology
to determine critical nursing shortages
across a variety of health care settings.
Ultimately, this research will support
the development of a comprehensive method
for identifying the health care facilities
and agencies with critical shortages of
RNs. This will permit more effective targeting
of Federal and other resources to encourage
service-obligated RNs to work in the facilities
with the greatest needs.
b. Expert Advisory
Panels
The study was conducted under the guidance
of four expert advisory panels, one for
each of four types of health care organizations:
hospitals, home health agencies, nursing
homes, and public health agencies. The
names of the panelists can be found in
Appendix B.
These panels met face-to-face twice.
The first meetings were held separately
early in the study to discuss preliminary
findings and agree on strategies for accomplishing
study goals and objectives. The second
meeting convened all the panels together
toward the end of the study to gain the
benefit of cross-fertilization of ideas.
In between these meetings the panelists
were invited to participate in two conference
calls in which interim progress reports
were provided to solicit feedback and
suggestions.
c. Guiding Principles
An important outcome of the initial meetings
of the advisory panels was agreement on
a list of “guiding principles” to inform
and direct our efforts. These principles
can be roughly classified as relating
to theoretical, practical, or fairness
concerns. The list also included some
specific recommendations about methodology.
The theoretical principles and ideals
included:
- Context: facility within community.
Both facility and community characteristics
must be considered, but community characteristics
are more important than facility characteristics.
- Demand over need. Analyses
should primarily focus on employer demand
for RNs (e.g., what the local labor
market will actually support) rather
than the health needs of the population.
High-need areas that have no resources
or infrastructure to employ additional
RNs would find little benefit in the
NELRP program.
- Identify standards for data.
Ultimately, it will be important to
upgrade Federal, state, and local data
systems to support better planning for
the nursing workforce, including the
designation of facilities and communities
with shortages of RNs.
- Consider facility culture.
Some facilities may experience high
RN vacancies not because of difficulties
recruiting RNs, but because of persistent
RN turnover due to problems of organizational
culture within the facility (e.g., poor
management). This is not a “shortage”
issue, and the NELRP program is not
intended to address such problems.
- Define shortage based on outcomes.
Theoretically, a facility can be said
to have “too few” RNs when there are
not enough RNs for the facility to effectively
function. This will be observed in certain
outcome measures relating to quality
of care and facility functioning.
The principles and ideals relating to
practical concerns included:
- Low administrative burden on facilities
and HRSA. Data used in the final
methodology should not require a large-scale
data collection or manipulation.
- Applicable to all facility types.
The final shortage methodology should
be applicable to and appropriate for
all facility types.
- Readily available data over time.
Ideally, the final methodology should
be supported by existing data that are
easy to access and available over time
for updating.
- Commonly accepted data elements
and indicators. Using established
indicators of supply, demand, and shortage
is preferable to developing new ones.
- Easy to update to reflect changing
environment. Data used for identifying
shortages should be easy to update so
that designations can be periodically
reexamined.
The principles and ideals relating to
fairness included:
- Attention to rural and urban differences.
The shortage designation method should
not systematically disadvantage either
rural or urban facilities.
- Special needs of some facilities.
The shortage designation method should
recognize extenuating circumstances
(e.g., facing critical problems, serving
special populations).
- Case mix of patients. The
method should recognize that some facilities
have higher patient acuity than others,
which may signify that some facilities
require more intensive staffing.
- Accommodate data manipulation.
The method should minimize opportunities
for facilities and communities to “game”
the system to achieve a shortage designation.
Specific recommendations for the method
included:
- Look beyond clinical care.
It should be recognized that overall
demand for RNs extends beyond just those
at the bedside to those in non-clinical
positions.
- Consider overall staff mix.
Some employees may substitute for RNs
with other personnel. This may be more
or less appropriate depending upon the
facility type.
- Consider RN staff mix (e.g.,
specialty, education). Facilities with
enough RNs overall may still have a
shortage of RNs with certain credentials
or in some services (e.g., ICUs).
- Separate out different units within
hospital care. Different units have
different staffing needs (e.g. intensive
care units will require more RNs than
general medical-surgical units).
Most of these guiding principles were
addressed in at least some of the analyses,
either directly or indirectly, and many
are incorporated into the Preferred Method
proposed by the study.
d. Characteristics
of an Ideal Shortage Designation Method
Early in the study a number of characteristics
were identified as especially desirable
for any method to identify facilities
and communities with shortages of RNs.
These characteristics, some of which may
not be attainable, included:
- A common method to be used across
the nation;
- Ease of calculation of the RN shortage
index for individual facilities and
communities;
- Implementation using existing data
sets, with no additional data collection
required;
- Comparison of shortages of RNs both
within and between different types of
facilities;
- Comparison of RN shortages across
different states and other geographic
jurisdictions;
- Consistency of shortage severity
estimates with shortage assessments
by local experts;
- Identification of shortages in facilities
due to poor management; and
- Easy updates to the method to reflect
more recent conditions, situations,
and relationships.
B. Methods
and Models Using Facility Data
All of the analyses using facility data
are based on data sets from North Carolina
and North Dakota. These datasets included
a number of possible measures of nursing
shortages that could be used as dependent
variables:
Effects of Nursing Shortage on
Facility Operations. The surveys
asked respondents an open-ended question
about how nursing shortages have affected
the operations of their facility. Responses
were then coded into nine categories.
This was an interesting variable because
of in-depth discussions in the first advisory
panel meeting about how true measures
of a nursing shortage should be related
to patient care and facility operations.
Although subjective, this variable touched
on those issues. Caution was warranted,
however, because the question asked about
nursing shortage generally, and respondents
may have answered the question thinking
about LPNs as well as RNs, particularly
if they were from a setting that relies
heavily on LPNs (e.g., nursing homes).
Nonetheless, this variable was used as
the dependent variable in a series of
preliminary ordinary least squares (OLS)
regressions.
RN Vacancy Rates. Both
the NC and ND datasets included RN vacancy
rates. Many facilities, however, had vacancy
rates of 0, which limited the variation
in the variable. Interestingly, there
was very little correlation between RN
vacancy rates and the number of reported
effects of the nursing shortage, which
was cause to question the utility of the
consequences variable given its subjectivity.
Vacancy rates were also used as the dependent
variable in several OLS regressions.
RN Turnover Rates. Turnover
rates were not used in any of the in-depth
analyses. In the first set of advisory
panel meetings, the panelists pointed
out that facilities that had a genuinely
limited supply of RNs to draw from should
be separated from facilities in which
poor management led to large numbers of
departures. Turnover can certainly reflect
limited supply, but also seems likely
to reflect problems of organizational
culture, particularly in facilities that
had low vacancy rates but high turnover
(meaning that they had no trouble finding
RNs, but had trouble retaining them).
Time to Recruit RNs. Both
datasets contained information on the
average number of weeks reported to fill
RN vacancies. Although theoretically a
good indicator of shortage, the large
amount of missing responses for this variable
ruled it out for practical reasons.
Difficulty Recruiting RNs.
This ordinal variable was used in a series
of ordered probit models conducted as
part of the study. The variable used a
five-point Likert scale with categories:
Very Difficult, Difficult, Neutral, Easy,
and Very Easy.
OLS regression equations were estimated
to predict and explain the number of adverse
consequences and vacancy rates in all
four types of facilities in North Carolina.
First the models were estimated with both
facility- and county-level explanatory
variables, which was the ideal model.
In recognition of the fact that facility-level
variables were not available in most states,
an abbreviated model using only county-level
data was estimated for each facility type
as well.
The results of these models were not
particularly satisfying. Relatively few
variables were strongly correlated to
adverse consequences, and the explanatory
power of the models (as measured by the
R-squared statistic) was generally low.
Although there were some statistically
significant explanatory (independent)
variables in the models for both predicted
consequences and vacancy rates, the models
explained only a relatively small percentage
of the variation in the dependent variables.
The explanatory power was even smaller
when the facility-level variables (which
would not be available outside of NC and
ND without new data collection) were removed
from the models, and only community variables
were used.
The conclusion based on these models
is that the variables collected by North
Carolina were not adequate to accurately
predict and explain either adverse consequences
or vacancy rates. That said, the results
did reveal new insights about the supply
of and demand for RNs. Thus the research
findings should be of interest to students
of the nursing workforce. A journal article
on this aspect of the study is planned.
The next set of models estimated for
North Carolina used the dependent variable
of difficulty recruiting RNs. Although
this variable was not available for RNs
overall, facilities in NC did rate RN
recruiting difficulty on a scale of one
to five for several types of RNs in several
types of units (e.g., staff RNs in ICUs,
nurse managers in ob/gyn floors, etc.).
To translate this set of ratings into
a single summary variable, a median value
was calculated for all the positions that
each facility had provided. Although few
facilities had valid values for all of
the different categories of hires because
they had not recruited for particular
positions in the past year, the median
did provide an estimate of the overall
difficulty.
A series of ordered probit models were
estimated to predict and explain variations
in this new median self-reported difficulty
in recruiting RNs. Coefficients for the
different explanatory and independent
variables were estimated for the four
facility types both separately and together
(to predict recruiting difficulty relative
to facilities of their own type and relative
to all facilities). The facility-specific
models are summarized in detail later
in the report.
These models showed promise in explaining
difficulty recruiting RNs. Nonetheless,
the models were dependent upon a number
of facility-level variables, and it was
not clear whether a subjective assessment
of the difficult recruiting was an adequate
basis for rating nursing shortages in
facilities.
To address some of the questions regarding
the adequacy of the “recruiting difficulty”
variable, project staff conducted a formal
validation of the “recruiting difficulty
models” with a series of follow-up calls
to those facilities that reported the
most and least difficulty recruiting RNs.
This “blinded” process was conducted with
the cooperation of the North Carolina
Center for Nursing (NCCN), which provided
contact information for those facilities
without linking them to the identifiers
in order to preserve the confidentiality
of the data provided on the original survey.
The interviewer asked for a retrospective
evaluation of difficulty recruiting RNs
in 2004 (the data year used in the analysis).
To control for the possibility that people
would provide retrospective data based
on the current situation, an assessment
of the current difficulty recruiting RNs
was also obtained.
The Spearman rank order correlation between
the original data reported in 2004 and
retrospective data obtained from 48 of
80 facilities through the validation process
was 0.347
(p = 0.016), an indication that the difficulty
recruiting RNs was a less than ideal measure
of shortage. Not only was the difficulty
recruiting in 2004 from the interviews
not highly correlated with the original
assessments made in 2004, but it also
was not highly correlated with current
difficulty.
Despite the fact that the correlation
was statistically significant, the conclusion
based on this validation process was that
subjective indicators of shortage were
likely to be too highly influenced by
personal judgments and biases of the person
completing the survey (e.g., overall disposition,
momentary mood) to justify using them
as the basis for a nursing shortage assessment
and designation process.
Another attempt to validate the recruiting
difficulty models involved applying the
results of the North Carolina models to
another state. The coefficients from the
NC ordered probit models were applied
to comparable data from North Dakota to
compare predicted to actual reported recruiting
difficulty. The coefficients from the
NC models proved to be a poor basis for
predicting recruiting difficulty in ND.
This raised serious questions about the
possibility of using coefficients from
one state to predict or estimate the extent
of shortages in another state. Although
further investigation might reveal that
coefficients from one state might be used
in some other state with similar demographic
characteristics, interstate variations
in health care and labor market environments
seem to preclude nationwide use of a model
constructed based on data from only one
state.
It was hypothesized that the relatively
small sample size for models based solely
on data from North Carolina might have
contributed to the limited number of statistically
significant coefficients, and that increasing
the number of cases might yield better
results. This hypothesis led to a final
set of models in the study incorporating
facility-level data and models based on
a combined data set from both North Carolina
and North Dakota. OLS regression models
were estimated to predict vacancy rates
at facilities in those two states combined.
The hypothesis, in fact, proved to be
true. Models based on the combined dataset
revealed a greater number of statistically
significant explanatory variables for
RN vacancy rates than models for either
state alone. The overall explanatory power
of these models remained only moderate,
however, with much unexplained variation
in vacancy rates. The long-term care model,
in particular, had very limited explanatory
power (R2 = 0.238). Furthermore,
these models continued to rely heavily
on facility-specific data that would be
difficult to obtain for a national shortage
designation method.
C. Geography-Based
Models
Given the practical and methodological
shortcomings evident in the analyses using
facility-level data, the project team
shifted its attention to models based
on only county-level data that were nationally
available and frequently updated. This
shift seemed justified theoretically as
well, because the inability of a facility
to recruit and retain RNs in a county
with sufficient overall supply of RNs
may be a result of organizational culture
rather than a genuine shortage. Limiting
analyses to easily obtainable county level
data seemed to serve these ends better
than further pursuit of models incorporating
facility-level data.
There are limitations and challenges
to a method based solely on geographic
factors. For one, patterns of RN employment
and health service utilization often transcend
county (and state) lines. Knowing where
RNs and patients live does not necessary
tell researchers where services were provided
or received, and thus where shortages
actually existed.
Furthermore, the use of county-level
data can mask large differences in facilities
within counties. This is particularly
true in the largest metropolitan counties.
For example, New York County (Manhattan)
may not meet the criteria for worst county-level
RN shortage, but this ignores the fact
that some facilities within Manhattan
have a much harder time recruiting RNs
than others (e.g., public facilities,
those located in neighborhoods perceived
as unsafe). Geography-based methodologies
also may not adequately account for special
circumstances specific to facilities.
Regardless of whether a facility is in
a large county or not, it may have extenuating
circumstances. There may be adequate numbers
of RNs in the county, for example, but
it may still be difficult to recruit RNs
to work with the homeless.
Supplementing geography-based models
with other procedures can minimize some
of these limitations. Primary care Health
Professional Shortage Areas (HPSAs) are
currently designated based on geography-level
characteristics, on facility-level characteristics,
or on service to special populations.
A similar tiered process could be developed
for nursing shortage designations. Geographic
designations could also be supplemented
with an application process that allows
facilities to submit facility-specific
data. Special rules could be established
to address sub-county variations in large
urban areas (e.g., certain facilities
in counties with population greater than
one million—public, in a HPSA, or in a
high-poverty Census tract—might automatically
qualify).
One thing that emerged clearly in the
analyses of facility-level data is that
certain types of facilities were disadvantaged
in the competition for RNs relative to
others. The current methodology for awarding
nursing loan repayment funds is based
on categories of facilities, and this
could be preserved so that certain types
of facilities continue to receive preference,
but in combination with geographic designations.
Geographic designations could also be
combined with facility type, in recognition
of the fact that certain types of facilities
(e.g., long-term care) may face greater
disadvantages than others (e.g., hospitals).
Facilities located in shortage counties
could be given priority based on facility
type, or conversely, facilities within
priority categories (e.g., disproportionate
share hospitals, community health centers)
could be given priority designations based
on county-level shortages.
An application procedure would allow
facilities that feel they have been unfairly
disadvantaged by a county-level designation
to submit facility-level data to document
their situation. This would ease the burden
on HRSA because most designations would
be based on geography, but facilities
with special circumstances would be given
an opportunity to appeal disqualification
based on geographic criteria alone.
The counts of RNs by county were taken
from the 2000 U.S. Census long-form data,
which is a 1-in-6 sample of the U.S. population.
These data gave RNs by county of residence,
not employment, and were less accurate
when the actual number of RNs in the county
was low (due to sampling error), but this
was probably the best source available
for county-level counts of RNs nationally.
In larger counties, the sample size should
be sufficiently accurate. But in smaller
counties, sampling error could have the
effect of either undercounting or overcounting
RNs. One person in the sample represents,
on average, six people. If a small county
has 102 RNs, theoretically one would expect
17 to be selected by the Census sample.
If only 13 were in fact selected, the
county would appear to have only 78 RNs,
and might inappropriately qualify as a
shortage county. On the other hand, if
20 were selected, the county would appear
to have 120 RNs, which might prevent it
from qualifying as a shortage county.
These kinds of sampling errors would be
random and not systematic, so less populous
counties should not be consistently advantaged
or disadvantaged by the method.
It is important that any method used
by HRSA be easily updated using existing
sources of data. Updating the decennial
U.S. Census data can only be done every
ten years, which creates estimation problems
that grow over time, especially for counties
that are rapidly growing or shrinking.
Starting in 2008 another option will become
available when the Census Bureau’s American
Community Survey (ACS) begins to provide
estimates for smaller areas using three-year
moving averages. Although the ACS sample
will be smaller than the Census long-form
data, it will be larger than any other
interim data set. Each person sampled
in the ACS in one year will represent
more than 100 people, and if three years
of data are combined, one will represent
about 33.
Estimates of where RNs live were inadequate
measures of supply because in some areas
commuting inflows or outflows were very
substantial. For example, only 16% of
workers in New York County in 2000 actually
resided in New York County. Using numbers
of RNs living in New York County would
thus substantially overestimate the degree
of shortage in that county.
The U.S. Census Bureau provides data
collected in the decennial census on commuting
flows between every pair of counties in
the U.S. From these data, commuting inflow
was estimated based on the percentage
of persons employed in county who lived
in a different county, and commuting outflow
was calculated based on the percentage
of employed residents of the county who
worked in a different county. These rates
of county inflow and outflow were applied
to RNs on the assumption that RN commuting
patterns were not different from commuting
patterns overall. (Preliminary analyses
did not indicate that RNs were any more
or less likely to work outside of their
county of residence.)
There are a number of ways to conceptualize
and measure RN supply at the county level,
ranging from simple to sophisticated.
All of the methods described below were
calculated using RN supply data adjusted
for commuting patterns.
a. RNs to Population
Ratio Method
This method is based upon the assumption
that RNs should be evenly distributed
across the U.S. in direct proportion to
population (e.g., that 70 people in Los
Angeles County, California require the
same number of RNs as the 70 people who
make up the entire population of Loving
County, Texas). The estimated number of
RNs required in a county is calculated
based on population need rather than demand
for RNs created by the existing healthcare
infrastructure, and assumes that people
receive nursing services where they live.
This ratio is very simple to compute
(#RNs/#Population) and the data needs
are also relatively clear. On the other
hand, this ratio is also very crude, ignoring
actual use of services (i.e., where people
actually receive care), and demographic
variations in health care needs (e.g.,
the greater needs of the older adults).
b. RNs to Adjusted
Population Method
The project team explored two methods
of adjusting the population. The first
was based on rates of primary care utilization
by gender and age (with weights based
on the new primary care HPSA methodology)
and the second was based on rates of utilization
of multiples types of services based on
age alone (with weights based on age-specific
utilization rates for different types
of services, gleaned from a variety of
sources [most commonly Health, United
States, 2005].
Because it accounts for population demographics,
this method, which assumes that age-specific
patterns do not vary across counties,
should more accurately reflect population
need than a simple RN to population ratio.
However, this method, like the first,
is based on estimated need for RNs rather
than estimated demand for RNs.
c. RN to Physician
Ratio
Both previous methods fail to account
for the location of health care infrastructure.
Regardless of the needs of the population,
if an area has no health care employers
to hire RNs, there is no labor market
demand for RNs and therefore no shortage.
Places with more health care employers
should, however, have more physicians,
so physician supply can be used as a crude
proxy for RN employer demand.
On the other hand, the net effect of
this method is that areas that have shortages
of both physicians and RNs may appear
comparable to areas that have surpluses
of both physicians and RNs if the ratios
are similar. This is of particular concern
because physician shortage areas may have
the greatest need for RNs to help provide
basic primary care services. This raises
the RN shortage standard for exactly those
counties—they must be short of RNs relative
to the number of physicians when they
are already short of physicians.
d. County Cluster
Adjustments
All of the previous methods discussed
ignore the flow of patients between adjacent
counties to receive health care. An attempt
was made to adjust for this by recalculating
the previous ratios based on county clusters
(RN, population, and/or physician counts
summed for each county and its contiguous
counties). The effect of this adjustment
was higher shortage scores for nurse-poor
counties surrounded by other nurse-poor
counties, compared to nurse-poor counties
surrounded by nurse-rich counties. This
is theoretically appropriate in that it
accounts for the unavailability of RNs
in neighboring counties as well as in
counties of residence.
This method showed some promise, but
it still did not address some of the fundamental
problems of the previous ratio methods.
Furthermore, it did not account for the
effects of multiple counties drawing on
each others’ resources. For example, it
is tempting to say that County A’s shortage
really isn’t so bad because it is bordered
on the west by County B, which has a surplus
of RNs. The situation of both County A
and County B would be accounted for in
County A’s county cluster, but what would
not be accounted for is the possibility
that County B is bordered on the west
by County C, which is also short of RNs
and draws on County B’s resources. County
B’s surplus may be sufficient to share
between its own population and County
A’s population, but not between its own
population, County B’s population, and
County C’s population.
e. Cross-County
Patient Flow Adjustments
Another attempt to adjust for the flow
of patients between counties involved
adjusting population figures based upon
commuting flows. This assumed that the
flows of patients seeking health care
services were similar to those for commuting
in general, and that areas that attracted
more commuters had more health care infrastructure
and would also attract more health care
consumers. Unfortunately, it was not clear
that this is always a reasonable assumption.
It seemed likely to be true for many counties,
but may not be true for some (particularly
counties with large outflows of “extreme
commuters” who travel more than sixty
minutes to their jobs).
After reviewing the various versions
of these ratio models, it was unclear
whether county clusters or adjustments
for cross-county patient flows were consistently
an improvement on base ratios. Ultimately,
it was concluded that an ideal method
should use actual measures of health care
utilization rather than attempting to
estimate patient flows.
f. Factor Analysis
of Nursing Shortage Indicators
A more sophisticated attempt to create
a typology of counties based on the RN
labor market involved factor analysis,
a more advanced statistical technique
used to collapse a large set of characteristics
of objects (counties in this case) into
a smaller set of “factors” that represent
different aspects of the objects. In this
case, different characteristics of counties
related to the supply of and demand for
RNs (e.g., #RNs per capita, per capita
income) load onto different factors that
represent different aspects of the supply
and demand for RNs (e.g., a factor related
to the economic conditions in the county).
This technique identified three broad
factors relevant to nursing shortages
at the county level: RNs relative to infrastructure
(demand); RNs relative to population (need);
and economic conditions. Based on the
factor analysis results, a typology of
eight categories was created based on
a binary split of the scores on the three
dimensions. The counties with the greatest
shortages were low on all three factors
(i.e., category 111), indicating high
levels of unmet need, unmet demand, and
socioeconomic disadvantage. The counties
with the least shortages were high on
all three factors (i.e., category 222).
This analysis showed promise in theory,
but was based on primary care utilization,
with no basis for examining long-term
care, home health care, or public agency
services, and no way of reflecting variations
in staffing intensity across types of
care. While acute care hospitals are the
primary driver of RN demand, the focus
on hospital care does not make this method
applicable to counties without hospitals.
D. Preferred
Method
Staff members of the Center for Health
Workforce Studies have been working with
the Lewin Group on the update of the HRSA
Nurse Supply Model (NSM) and Nurse Demand
Model (NDM). Although the exact analyses
included in the NDM could not be replicated
at the county level due to data constraints,
the basic logic employed in the NDM was
very useful in thinking about demand for
RNs.
The project staff decided to develop
a simplified version of the NDM model
to: 1) estimate health care utilization
in different settings for counties (e.g.,
inpatient days); 2) estimate current national
RN staffing by setting (e.g., RNs working
in inpatient units); 3) calculate national
RN staffing intensity for each setting
(e.g., RNs per inpatient day); 4) apply
national RN staffing intensity ratios
to measures of utilization for each county;
and 5) sum estimate demand for each setting
to produce overall RN demand for individual
counties. Each step is summarized briefly
below.
The data on county-level health care
utilization primarily came from the Area
Resource File (ARF). The ARF included
data on:
- Short-term inpatient days (non-psychiatric
hospitals)
- Long-term inpatient days (non-psychiatric
hospitals)
- Psychiatric hospital inpatient days
- Nursing home unit inpatient days
(hospitals)
- Outpatient visits (non-emergency)
- Emergency department visits
The number of (non-hospital) nursing
home residents in a county was obtained
from the 2000 U.S. Census. This was based
on the Census short-form data, which is
theoretically obtained from 100% of the
U.S. population.
The number of home health patients per
county was estimated using the age and
gender distribution of the population,
based upon national age-specific and gender-specific
utilization rates from the Centers for
Disease Control and Prevention (CDC).
Although this estimate was based upon
population characteristics rather than
actual use of services, home health patients
by definition were receiving services
where they live, so this was somewhat
less problematic than estimating other
types of utilization based upon population
characteristics.
Data for current levels of RN staffing
by setting were taken from the 2000 NSSRN,
which included data on the number of RNs
employed in the following types of care:
- Short-term inpatient (non-psychiatric
hospitals)
- Long-term inpatient (non-psychiatric
hospitals)
- Psychiatric inpatient (non-Federal)
- Nursing home unit (hospital)
- Outpatient (non-emergency)
- Emergency outpatient
- Non-hospital nursing home
- Home health
- Nurse education
- Public/community health
- School health
- Occupational health
- Non-hospital ambulatory care
- Other nursing care
These numbers were combined with the
national utilization data described above
to compute national RN staffing for the
various types of care.
These national staffing ratios were then
applied to the utilization rates for each
county. For example, the national ratio
was 4.97 RNs working in hospital inpatient
units per inpatient day. If County A has
12,000 inpatient days per year, their
demand for RNs in inpatient units is estimated
at 59.6 (4.97 x [12,000/1,000]).
Overall RN demand for the county was
obtained by summing RN demand in the county
across all settings. This procedure also
opens the possibility of comparing setting-specific
demand to setting-specific supply, if
data on RN supply by setting are available
at the county level.
RN shortages were then measured as follows:
RN shortage |
= |
Estimated demand for RNs in the
county minus the number of RNs in
the county (adjusted for commuting
patterns). |
Raw shortage estimates were then standardized
as a percent of demand. A table showing
the numerical results for all counties
in the U.S. can be found in Appendix E.
This table is presented as a series of
maps for all of the states in Appendix
F. The counties with the greatest shortages
are shaded black.
This method has advantages over any of
the other methods examined in this study,
especially in relation to the guiding
principles initially proposed for the
study:
- It uses nationally available data
that is periodically updated.
- It uses actual health care utilization
patterns by county.
- It accounts for multiple types of
care (including non-clinical services).
- It accounts for differences in RN
staffing intensity across settings.
Some limitations persist, however. The
method does not account for county or
state variations in health systems (e.g.,
HMO penetration, use of LPNs), and does
not account for patient acuity within
types of care. Furthermore, it assumes
current RN staffing levels were adequate
at the national level in 2000, which may
not have been the case.
The NDM uses factors such as HMO penetration
and LPN staffing in regressions to adjust
estimated staffing intensity and make
it specific to each county rather than
applying national ratios. A similar procedure
might eventually be used to do the same
thing here. In fact, the new NDM model
might be used directly to support this
entire approach.
E. Additional
Analyses and Explorations
Two suggestions were made at the final
advisory committee meeting to improve
the Preferred Method. Each is summarized
briefly below.
Perhaps the greatest shortcoming of the
Preferred Method is that it does not adequately
account for patient acuity. This leads
to underestimates of RN demand and need
in counties with large medical centers
with trauma units, which might be expected
to have higher levels of patient acuity
on average than small community hospitals.
Related to this, larger hospitals may
also have more patients admitted for complex
surgeries and may require larger surgical
staffs (including OR RNs) than their smaller
counterparts.
Study staff performed a number of analyses
to determine whether the Preferred Method
could be improved by adjusting for patient
acuity. These analyses included using
more detailed categories of hospital beds,
including medical and surgical intensive
care beds, cardiac intensive care beds,
neonatal intensive care beds, neonatal
intermediate care beds, pediatric intensive
care beds, burn care beds, other special
care beds, and other intensive care beds.
Such breakouts can be used to disaggregate
inpatient days into ICU days and regular
care days.
The net effect of this adjustment was
to reduce the estimated nursing shortage
for many counties, but to increase it
for few. Unfortunately, this approach
suffered from several limitations. Data
on the numbers of beds in different categories
were not available in the ARF for hospitals
in about 10% of counties. In addition,
bed type breakouts were not available
for short-term non-general hospitals,
which may also have ICUs and operating
rooms.
Another limitation was that while RNs
cannot be separated by general versus
non-general short-term hospitals, so RNs
in ICUs in both types of hospitals will
be factored into the staffing ratio for
ICU, but the inpatient days in short-term
non-general hospitals cannot be adjusted
down by parsing out the ICU bed days.
Despite these limitations, this adjustment
has promise and should be considered as
the theoretical standard, even though
currently available data do not support
its use in practice.
The original version of the Preferred
Method assumed that RN commuting patterns
were similar to those of the overall workforce.
This is generally true in the aggregate—RNs
are no more or less likely than other
workers to work outside the county where
they live. At the county level, however,
RN commuting patterns sometimes varied
dramatically from the patterns for all
workers. A number of models were developed
to better understand RN commuting patterns.
Among the independent, explanatory variables
used in these models were:
- The commuting patterns of all workers;
- Opportunities for RN employment available
in particular counties;
- Counties where resident RNs were
in short supply relative to service
use;
- Whether the county was a whole-county
HPSA;
- The major industry in the county;
- Whether the county was a persistent
poverty county; and
- The rural-urban characteristics of
the county (population, proximity to
a metro area).
The most accurate method for estimating
RN commuting varied by county type. In
metro counties, the commuting flow of
all workers was the most accurate estimate
of the three 39% of the time. In counties
adjacent to metro areas, the model for
all counties was the most accurate 47%
of the time. In counties not adjacent
to metro areas, the best estimate was
the Rural Urban Classification Code (RUCC)-specific
estimate 51% of the time.
In general, RN commuting patterns depended
more on characteristics of counties than
on characteristics of RNs (e.g., gender,
income level, etc.). However, the “best”
estimate was often better than the “next
best” estimate by only a point or two.
F. Study Recommendations
The study identified six recommendations
for HRSA and other organizations to consider
as they attempt to identify facilities
with critical shortages of RNs accurately
and reliably. Several of these recommendations
are presented below.
- Of the methods examined in this study,
the Preferred Method outlined in this
report is the best choice for assessing
the severity of nursing shortages in
counties in the U.S. It meets more of
the desirable criteria identified by
the study advisory panels and it can
be implemented with currently available
data. Additional steps outlined below
could further improve the effectiveness
of this method.
- Additional review and validation
of the Preferred Method is required
by stakeholders who would be affected
by its implementation. Ideally, this
validation should take place in a representative
sample of states, counties, and facilities
across the U.S., and would address the
following kinds of questions:
- Are facilities and counties classified
correctly by the method? Is the
method biased in favor of or against
a type of facility, type of community
or county, or region of the country?
If so, how should the bias be addressed
or overcome?
- Are the basic data required to
support the method both available
and accurate for all regions and
states in the U.S.? How should sampling
errors for small rural counties
be addressed?
- How should facilities that have
nursing shortages primarily due
to persistent poor management be
dealt with in the method? What criteria
should be used to identify facilities
with poor management and should
their identities be made public?
- Should the method be supplemented
by some sort of appeals process
to permit a facility with a genuine
shortage to qualify for NELRP and
NSSP even though the method does
not place it in a sufficiently severe
shortage category?
- Should the method identify just
enough “severe shortage” counties
and facilities to allocate all NELRP
and NSSP recipients and other related
funds based on nursing shortages?
Or should it identify extra facilities
to provide flexibility to account
for other factors?
- More accurate estimates of RN employment
and supply should be developed at the
county level. This may not require new
data collection if appropriate refinements
can be made to the sampling frames for
existing datasets, especially the NSSRN.
- More research should be conducted
on factors related to the demand for
RNs, including HMO penetration, alternate
service delivery models, the use of
LPNs and other types of staff, and new
diagnostic and treatment technologies.
Factor analysis may be a fruitful avenue
for additional research. Another promising
avenue for research will open up when
the revised Nursing Demand Model becomes
available sometime in 2007.
- More research should be conducted
on factors related to the supply of
RNs, including RN commuting patterns,
how very rural communities can recruit
and retain RNs, how inner-city facilities
can recruit and retain RNs, etc. One
promising avenue for research will open
up when the revised Nursing Supply Model
becomes available sometime in 2007.
- Because shortcomings in available
data and extenuating circumstances might
cause certain facilities to be assigned
the wrong shortage designation, a formal
protocol by which facilities can appeal
and correct their shortage designation
should be developed. The development
process should consider a variety of
appeal options including single facility
designation changes and blanket designation
changes for entire classes of facilities.
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