Analysis of the Joint Distribution of Disproportionate Share Hospital Payments. Three State Analysis of Alternative DSH Policies


By linking inpatient claims and financial data for the hospitals in California, New York, and Wisconsin, we were able to:

  • Trace the actual distribution of “new” Medicaid DSH payments.
  • Assess how well the claims-generated measures of low-income patients correspond to measures generated from financial data (e.g. proportion of self-pay and no-charge patients relative to bad debt and uncompensated care costs); and,
  • Compare how well the current and alternative DSH allocation policies target financially vulnerable safety net hospitals. In this regard, we were able to examine how funds might be redistributed across classes of hospitals, but because only three states were involved, we could not estimate the redistributions that might occur across states under the different alternatives.

The findings from the three state analysis indicate that an across-the-board assumption regarding “new” DSH funds such as that used in Table ES.3 is not borne out at the hospital level. In California, intergovernmental transfers from county-owned hospitals and the University of California clinics financed over 56 percent of total DSH payments; $936.3 million of $2,153.8 million reported in DSH payments were “new” DSH funds. In New York, the hospital inpatient assessment contributed 13% of the funds to support the indigent care pool, with the remaining coming from payer contributions; $1,169.5 of the $1,350.5 million in DSH payments received by New York hospitals was “new” money. All DSH funds received by Wisconsin hospitals were “new” ($11.2 million).

When we examined the relationship between financial risk as a percentage of operating expenses and the percentage of care provided to low-income patients (Table ES.4), we found only a modest correlation. Consistent with the HCUP findings, there was a stronger correlation between the low-income patient utilization and revenue measures.

Table ES.4 
Selected Measures of Serving Low-Income Patients: 
Hospital-Weighted Means and Correlation Between Measures Using 3-State Analysis File
  Ratio of FR to Operating Expenses % Low-income days % Low-income revenue % Non-Medicare low-income days % Non-Medicare low-income revenue
MEAN 0.077 0.256 0.246 0.210 0.212
STD 0.071 0.184 0.178 0.169 0.166
N hospitals 614 614 614 614 614
Pearson’s Correlation Coefficient**
Ratio of FR to Operating Expenses 1.000 0.567 0.591 0.560 0.579
% Low-income days   1.000 0.826 0.979 0.786
% Low-income revenue     1.000 0.811 0.984
% Non-Medicare low-income days       1.000 0.803
% Non-Medicare low-income revenue         1.000

**all values p<.0001

Finally, we examined the relationship between the DSH allocations, financial risk and the hospital’s income net of DSH. We expected to find a negative correlation between the hospital’s ratio of revenues (net of DSH) to expenses and its ratio of financial risk to operating cost; that is, hospitals with high financial risk have more difficulty generating revenues to cover their expenses. While the correlation was in the expected direction, it was modest (-.407). The correlation was -.302 between the DSH funds a hospital receives under current Medicare and Medicaid policies and its ratio of revenues to expenses. When the analysis is limited to the 307 safety net hospitals in the three states (Table ES.5), the correlation between net income and current DSH funding policies is stronger for current DSH policies than alternative policies.

Table ES.5
Safety Net Hospitals in 3 State Analysis Correlation Between Financial Status Measures and Alternative DSH Allocation Policies
  Income net DSH ($ mill) Financial risk 
($ mill)
Joint DSH funds 
($ mill)
Medicaid New DSH 
($ mill)
Medicare DSH 
($ mill)
Sim A
($ mill)
Sim B
($ mill)
Sim C
($ mill)
MEAN -8.351 12.025 8.568 4.752 3.816 8.917 8.349 8.265
STD 26.222 19.924 16.025 13.784 4.902 17.778 18.361 15.532
N 307 307 307 307 307 307 307 307
Pearson’s Correlation Coefficient**
Income net DSH 1.00 -0.57 -0.52 -0.41 -0.52 -0.24 -0.29 -0.44
Financial risk   1.00 0.74 0.64 0.63 0.73 0.73 0.83
Joint DSH funds     1.00 0.96 0.58 0.80 0.81 0.78
New Medicaid funds       1.00 0.31 0.79 0.81 0.77
Current law Medicare funds         1.00 0.40 0.35 0.40
Sim A: % Non-Medicare low-income days w/WI           1.00 0.96 0.81
Sim B: % Non-Medicare low-income revenues             1.00 0.85
Sim C: Financial risk               1.00

** All values p<.0001

The mean DSH payments reported in Table ES.5 pertain to safety net hospitals only, which are defined for purposes of this analysis as hospitals with at least 20 percent of their inpatient days attributable to low-income patients. The baseline used in the simulations totaled $2,748 billion across all three states. If all DSH funds had been distributed to safety net hospitals, a hospital would have received on average $8.951 million.4 The difference between this amount and the mean payment in each simulation is accounted for by DSH funds distributed to hospitals with less than 20 percent of their inpatient days attributable to low-income patients. The differences between Simulation A and Simulation B highlight the differences between allocations based solely on inpatient care and allocations that take into account both inpatient and outpatient care. Including all care only slightly improves the targeting of DSH funds to financially vulnerable safety net hospitals. Simulation C allocates funds based on financial risk.

4.  The baseline for the simulations was current law Medicare and the federal share of DSH payments.

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