Given the practical and methodological shortcomings evident in the analyses using facility-level data, the project team shifted its attention to models based only on 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 a model incorporating facility-level data.

A. 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 would provide facilities with special circumstances with an opportunity to qualify.

B. Measuring 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, which increases the potential for sampling errors in small counties. Each person sampled in the ACS in one year will represent more than 100 people.

C. 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.)

D. Geography-Based Methods

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 below were calculated using RN supply data adjusted for commuting patterns.

1. 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 health care 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).

2. 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 estimated 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.

3. 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 are short on both physicians and RNs appear comparable to areas that have surpluses of both physicians and RNs if the ratio is similar. This is particularly concerning because physician shortage areas may have the greatest need for RNs to provide basic primary care. 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.

4. 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 was theoretically appropriate in that it accounts for the unavailability of RNs in neighboring counties as well as in the county 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 isn’t really 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.

5. 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 for seeking health care services were similar to those for commuting in general, and that areas that drew more commuters had more health care infrastructure and would also draw more health care consumers. Unfortunately, it was not clear that this is 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. It was concluded that an ideal method should use actual measures of health care utilization rather than attempting to estimate patient flows.

6. 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, an 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 nurses (e.g., #RNs per capita, per capita income) load into different factors that represent different aspects of the supply and demand for nurses (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. These factors are shown in Table 12.

Table 12. Standardized Factor Analysis Coefficients Related to Nursing Shortages in Counties in the U.S.

Note: The three factors can explain 50.3 percent of total variation of all variables

Using factor analysis, a typology of eight categories was created based upon their scores on the three dimensions (Table 13). Using this approach, 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).

Table 13. Numbers and Percentages of Counties in Factor Analysis Categories, by Census Division

Note: ^(a) An example of how to interpret the category: 121 means F1<median, F2>median, F3<median

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.