Executive Summary

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.

1. Federal Initiatives to Address Nursing Shortages

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.

2. Study Overview

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.

1. Ordinary Least Squares (OLS) Regression Models

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.

2. Ordered Probit Models

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.

3. Validation of North Carolina Results

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.

4. Application of North Carolina Ordered Probit Coefficients in North Dakota

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.

5. OLS Regressions for Vacancy Rates Using Combined Data from NC and ND

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.

1. Limitations and Challenges

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.

2. Measuring the RN Supply at the County Level

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. 

3. Adjusting for Commuting

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.)

4. Methods Using Only Geographic Data

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.

1. Estimate Health Care Utilization

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.

2. Estimate Current National RN Staffing

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.

3. Estimating RN Demand by County.

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.

4. Use Supply of RNs to Estimate RN Shortages.

RN shortages were then measured as follows:

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.

1. Adjustments for Patient Acuity

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.

2. More Careful Analysis of Commuting Patterns

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.

1. 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.
2. 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?
3. 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.
4. 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.
5. 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.
6. 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.