Medicare prospective payment without separate urban and rural rates

Health Care Financing Review,  Winter, 1992  by Sheila M. O'Dougherty,  Philip G. Cotterill,  Steven Phillips,  Elizabeth Richter,  Nancy De Lew,  Barbara Wynn,  Thomas Ault

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Table 8 also shows that the DSH coefficient is more sensitive to the inclusion of urban-rural variables than the IME coefficient. The DSH coefficient falls from 0.464 to 0.216 when the urban-rural variables are added to the cost equation. Under current law, the 975 urban hospitals with 100 beds or more that qualify for DSH payments receive an average payment adjustment of 13.4 percent. For this group of hospitals, our regressions would generate average DSH adjustments of 12.9 and 5.8 percent. The 1,970 hospitals eligible for DSH payments based on our analysis (all urban hospitals with 100 beds or more) would receive mean DSH adjustments of 8.2 and 3.7 percent.

The coefficients of the large-urban and other-urban-area dummy variables are 0.140 and 0.093. The coefficients for large and other-urban areas imply that costs of hospitals in these areas are 15 and 9.4 percent higher than those of rural hospitals.

Simulation results

Regression analysis and payment simulations were used in concert to determine the combination of IME, DSH, and urban-rural payment adjustments that would yield the best payment system. Because of potential non-linearities and interactions among the variables, regression analysis alone will not necessarily yield a set of payment-to-cost ratios that demonstrate the desired balance among the hospital groups.

Table 9 reports the results of four simulations, in addition to the baseline case that incorporates all the case-level refinements (repeated from Table 5, column 5.) Columns 2, 3, and 4 are derived from the IME and DSH regressions already discussed. The simulation in column 2 uses IME and DSH factors based on the regression excluding urban-rural variables. Columns 3 and 4 are derived from the regression that includes urban-rural variables. In column 3, the urban-rural effects are ignored for payment purposes, whereas in column 4, the regression estimates of urban-rural effects are built into payments. As discussed later, the final simulation modifies the simulation in column 3 by paying a 3-percent payment add-on to hospitals in large urban areas.

[TABULAR DATA OMITTED]

The simulation in column 2 of Table 9 uses IME and DSH adjustments that incorporate urban-rural effects indirectly. The payment-to-cost ratios of the major teaching hospitals drop from 1.1388 to 1.0238, a reduction that is expected based on a comparison of the mean IME adjustment level under current law (30 percent) and the regression excluding urban-rural variables (18 percent). Corresponding to this reduction, the payment-to-cost ratio of non-teaching hospitals increases from 0.9655 to 0.9893, as payments are redistributed from teaching to non-teaching hospitals.

Compared with the teaching payments, which were reduced by 50 percent, DSH payments actually increase by 39 percent. One reason is that, as already noted, the number of urban hospitals with 100 beds or more receiving DSH payments increases from 975 (451 large-urban and 524 other-urban) to 1,970 (1,025 large-urban and 945 other-urban). Secondly, the mean DSH payment percentage for the 975 hospitals decreased only slightly from the current level to that based on the regression excluding urban-rural variables (13.4 to 12.9 percent). In contrast, there is a large decline in the teaching adjustment.