There were a number of outstanding issues related to the Preferred Method that required a closer look. While fully addressing some of these issues was beyond the scope and timeframe of the current study, limited analyses were performed to investigate some potential avenues for improving the Preferred Method to address these shortcomings. Two of the most important analyses are summarized below.

A. The Problem of Patient Acuity

Study staff did attempt to correct for patient acuity in hospital settings because hospital patient acuity will differ across counties in ways that may systematically disadvantage counties with major medical and trauma centers. ARF data was used to measure the number of surgeries and to estimate percent of inpatient days spent in the ICU. NSSRN data was used to estimate the number of RNs working in ICU units and operating rooms, and the steps in the earlier model were followed to obtain estimates of demand for operating room RNs and ICU RNs calculated separately from other hospital RNs. It was a problem, however, that many counties lacked accurate ICU bed data (especially large urban counties). Perhaps, as a result, this adjustment did not have the expected effects on RN demand estimates. In fact, it often resulted in lower rather than greater estimated demand for RNs.

Figure 8. Estimated RN Shortage Percentages for Counties in the U.S.

[D]

This additional adjustment for ICU and surgical services is theoretically important in that it is one of the few possible adjustments for acuity. While the quality of currently available data may prohibit incorporating the adjustment into a national methodology, hospitals that feel that their higher patient acuity has disadvantaged them in the standard process could potentially submit ICU and surgical data through an application and appeals process.

B. RN 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. Additional analyses summarized below revealed that RN commuting patterns depended more on county characteristics than on characteristics of RNs (e.g., gender, income level, etc.). These analyses were based on data from New York, North Carolina, and Mississippi, states for which data were available on both county of residence and county of employment.

1. Models to Predict RN Commuting Patterns

To help identify the factors related to RN commuting patterns, models based on county characteristics were developed. The commuting patterns of all workers had been, on average, a good proxy for the commuting patterns of RNs, so this was retained as one independent variable. This variable should reflect many of the primary drivers of commuting behavior (e.g., relative wages, cost of living, etc.). Another independent variable was assumed to be opportunities for RN employment in a particular county. This was measured by the extent to which the number of RNs living in a county compared to the estimated demand for RNs in that county (based on infrastructure and service use). Counties where resident RNs were in short supply relative to service use were expected to be net importers of RNs, while counties where resident RNs were more than sufficient for the county’s health care needs were expected to be net exporters of RNs.

Other factors included in the analysis were whether the county was a whole-county HPSA, the county’s major industry, and whether the county was a persistent poverty county. The rural-urban characteristics of the county (population size, proximity to a metropolitan area) were also accounted for, although these did not prove as crucial as expected (probably because they did not affect RN commuting any differently than overall commuting, which was already controlled for).

The intercept for the model was 0.495, indicating that if all other variables had a zero value, each resident RN would be equal to 0.495 RNs working in the county. The coefficient for overall commuting was 0.601, indicating that for every one percent increase in net incommuting, there would be a 0.601 unit increase in RN incommuting. The supply of resident RNs relative to estimated demand was negatively related to net incommuting (-0.148).

The percent of the population living in an urban area within the county was positively related to RN incommuting (0.001), but this was not statistically significant (p=0.059). For every increase of 10,000 population, RN incommuting increased by 0.0012. Whole-county HPSA status decreased net RN incommuting (-0.157), as did persistent poverty county status (-0.158) and dependence on manufacturing (-0.09).

There was an interesting interaction effect between population size and persistent poverty status: being a persistent poverty county had a greater depressant effect on RN incommuting in small population counties than in large population counties. This model had an adjusted R² of 0.702.

Table 15 shows the results of models estimated separately for groups of counties based on their relationship to a metropolitan area (part of a metropolitan area, adjacent to a metropolitan area, or not adjacent to a metropolitan area). Although the results show some potential to fine-tune the RN incommuting estimates for different groups of counties, the differences in the model coefficients were not dramatic. Differences in model fit were substantial, however. The model for counties not adjacent to a metropolitan area was the best fitting model (adjusted R² = 0.842). The model for metropolitan counties also fit well (R² = 0.805). The model for non-metropolitan counties adjacent to metropolitan areas, however, explained less variation (R² = 0.509).

Table 15. Ordinary Least Squares Regression Coefficients Predicting RN Incommuting, By Type of County

* p < 0.05
** p < 0.01
*** p < 0.001

Interestingly, the most accurate method for estimating commuting varied by county type. In metro counties, the commuting flow of all workers was the most accurate estimate of the three models 39% of the time; while in counties adjacent to metro areas, the model for all counties was the most accurate 47% of the time; and in counties not adjacent to metro areas, the best estimate was the RUCC-specific estimate 51% of the time.

In many cases, however, the “best” estimate was better than the “next best” estimate by only a point or two. When the variable used to evaluate was the percent of the time that an estimate differed by more than 10% from the actual RN commuting value, the all-county estimate was accurate more often for metro and adjacent-to-metro counties, while non-adjacent-to-metro counties did best when the RUCC-specific estimate was used. It was never preferable to use the overall commuting pattern.

Table 16. Percentage of Cases in Which Estimate Differs From Actual by More Than 10%

2. Using Commuting Patterns to Estimate RN Supply

Because the evaluation of the estimates using data from the counties from which they were derived was somewhat tautological, it was decided to assess whether these corrections brought estimates of RN employment by county closer to actual employment data in other states (for which real commuting patterns were not available). The states used in this preliminary validation process were Tennessee, Texas, Pennsylvania, South Dakota, and some counties in Iowa.

When compared to actual counts of RNs working in particular counties, the revised commuting adjustments did little to improve the supply estimates. The estimated supply was closer to the actual supply on average when overall commuting was used as the adjustment factor. There was some variation by the Rural Urban Classification Code (RUCC): the estimate of commuting based on the all-county model produced somewhat lower average differentials than other estimates for counties adjacent to metro areas, and somewhat lower absolute average differentials for counties not adjacent to metro areas.

It is important to remember that the accuracy of the commuting estimates is only one source of error in estimated supply of RNs working in a county. Another source of error is in the estimated numbers of RNs living in the counties in which they work. It may be possible in future work to estimate confidence intervals around commuting estimates and estimates of supply of resident RNs, and for shortage designations to be based on the lower of the two estimates of RNs based on the two confidence intervals. This would remove some of the disadvantage potentially faced by rural areas due to sampling errors, although it would increase the likelihood of designating some counties that should not qualify.