Medicare: Information on Medicare-Dependent Hospitals
Released: 2020-02-28T00:00:00

Letter

Background Payment for Hospital Services under Medicare

Under traditional Medicare, hospitals are paid for the inpatient and outpatient services they provide under two distinct payment systems.

Inpatient stays, including services incurred after being admitted to the hospital, are paid under the IPPS. Under this system, Medicare pays hospitals a flat fee per beneficiary stay, set in advance, with different amounts generally based on the beneficiary’s condition. Payment rates are also influenced by hospital-specific factors such as the relative hourly wage in the area where the hospital is located, and whether the hospital qualifies for other case- or hospital-specific additional payments.

Outpatient services, including services obtained through the emergency department or other services incurred without being admitted to the hospital, are paid under the outpatient prospective payment system. Under this system, Medicare pays hospitals a flat fee per service, set in advance, with different amounts for each type of service. As with the IPPS, payment rates are adjusted for geographic factors.

Congress has established payment adjustments for certain hospitals under the IPPS by changing the qualifying criteria for IPPS payment categories, creating and extending exceptions to IPPS rules, or exempting certain types of hospitals from the IPPS. These adjustments may help ensure beneficiary access to care or to help hospitals recruit and retain physicians and other medical professionals.

MDH Designation Eligibility Criteria

Created through the Omnibus Budget Reconciliation Act of 1989, the MDH designation is an example of how Congress can enhance payments to certain hospitals. To qualify as an MDH, a hospital must demonstrate that it is:

Medicare-dependent, defined as having at least 60 percent of their inpatient days or discharges attributable to Medicare beneficiaries; small, defined as having 100 or fewer beds; and rural, defined as being located in a rural area, though hospitals can also be eligible if they are located in a state without any rural areas.

CMS regulations provide that hospitals can meet the requirement of demonstrating a 60 percent Medicare share of days or discharges using two of the three most recently settled cost reports, or using cost reports from 1987 or 1988. We refer to hospitals that meet this criterion using 1987 or 1988 cost report data as “legacy MDHs.”

MDH Designation Payment Criteria and Payment Methodology

Some, but not all, MDHs are eligible to receive additional payment each year if they meet the payment criterion. Specifically, MDHs are assigned a payment rate—known as the hospital-specific rate (HSR)—based on their historic reported inpatient operating costs, trended forward to adjust for inflation and other factors, from one of three years (1982, 1987, or 2002). If the payment based on the HSR is higher than what the MDH would have otherwise received under IPPS, the MDH receives an additional payment. In this case, the MDH additional payment is calculated as 75 percent of the difference between the HSR and the IPPS amount. If the IPPS amount were higher than the HSR, the MDH would receive no additional payment. (See fig. 1.)

Hospitals with an MDH designation are also eligible to receive other benefits. For example, MDHs are eligible for a separate additional payment if the hospital experiences at least a 5 percent decline in inpatient volume due to circumstances beyond its control. The MDH program does not provide for additional payments for outpatient services.

The MDH Program Differs from Other Medicare Rural Hospital Payment Designations in Terms of Eligibility Criteria, Financial Benefit, Legislative Permanence, and Relative Size

In addition to the MDH designation, four other rural hospital designations exist: (1) critical access hospitals (CAH), (2) sole community hospitals (SCH), (3) low-volume adjustment hospitals (LVA), and (4) rural referral centers (RRC). Our review of CMS documentation shows that the MDH payment designation differs from the other rural payment designations in terms of eligibility criteria, financial benefit, extent of legislative permanence, and size—that is, the number of hospitals receiving the designation. (For detailed information on the five rural payment designations, see app. II.)

Eligibility Criteria. The MDH designation differs from the other designations in terms of eligibility criteria. As noted earlier, MDHs must have at least 60 percent of their inpatient days or discharges attributed to Medicare patients, must be small and, with few exceptions, rural. In contrast, both the SCH and CAH designations require hospitals to be remote rural hospitals (i.e., located a specified distance from the nearest hospital). Similarly, LVAs are generally required to be more than 15 miles from the nearest hospital. Rural hospital designations also differ in terms of eligibility criteria related to bed size. CAH-designated hospitals are required to have 25 inpatient beds or fewer, while MDHs must have 100 beds or fewer. RRCs must have at least 275 beds or meet other criteria, such as serving a high proportion of remote patients, among other things.

Financial Benefit. The MDH designation has a relatively small financial benefit compared to most of the other rural hospital designations, and the benefit only applies to costs associated with inpatient services. MDHs generally can only receive 75 percent of the difference between payment based on their HSR and the payment they would have otherwise received based on the IPPS rate as an additional payment added to their IPPS rate payment. In contrast, the SCH and CAH designations have both inpatient and outpatient payment benefits. Hospitals with an SCH designation can receive an additional payment added to their IPPS rate payment equal to 100 percent of the difference between payment based on the HSR and what the hospital would otherwise receive as payment based on the IPPS rate, as well as a 7.1 percent addition to their outpatient payments. The CAH designation results in the highest financial benefit by generally providing 101 percent of the hospital’s reported costs in the current year for both inpatient and outpatient Medicare services. LVAs generally can receive up to 25 percent in additional payments, and while RRCs receive no direct financial benefit, they are exempt from certain requirements related to geographic reclassification (as are SCHs).

Legislative Permanence. Unlike all but one other rural payment designation, the MDH program is a temporary program and must be extended periodically by Congress in order to continue. Historically, the extension by Congress has sometimes occurred after the program has expired and as a result there were temporary lapses in payments to MDH designated hospitals. The Bipartisan Budget Act of 2018 included a provision to extend the MDH program through fiscal year 2022. The only other designation that must be extended is the LVA designation. In 2010, the Patient Protection and Affordable Care Act temporarily expanded the LVA designation eligibility criteria to include hospitals with a higher volume of discharges and located closer to other hospitals than in previous years. These expanded eligibility criteria have been amended and extended through fiscal year 2022. If Congress does not extend the expanded eligibility criteria beyond fiscal year 2022, the LVA designation will return to the narrower eligibility criteria that were in place prior to the Patient Protection and Affordable Care Act.

Relative size and overlap. Of the 2,204 rural hospitals in fiscal year 2017, a relatively small share of these hospitals were MDHs. (See fig. 2.) In total, 138 hospitals, or 6.3 percent of those rural hospitals with at least one designation, were MDHs. In contrast, CAHs comprised the largest proportion of rural hospitals with a designation. In fiscal year 2017, 1,246 rural hospitals—or 56.5 percent of those rural hospitals with at least one designation—were CAHs. Of the five designations, three—CAHs, MDHs, and SCHs—are exclusive to each other, meaning a hospital can only have one of the three designations at any time. Hospitals designated as MDHs and SCHs may also be designated as LVAs, RRCs, or both. Approximately 75 percent of MDHs and 81 percent of SCHs had at least one concurrent designation in fiscal year 2017; in contrast, none of the CAHs received a secondary designation because CAHs are not eligible to receive other designations. Those MDHs with a concurrent designation consisted of 88 that had an LVA designation, 14 that had an RRC designation, and 2 that had both an LVA and RRC designation. (For detailed information on the 5 rural payment designations including LVA and RRC eligibility and financial benefit, see app. II.)

The Number of MDHs Declined over Time, As Did the Inpatient Share of Medicare Revenue and Profit Margins

From fiscal years 2011 through 2017, the number of MDHs declined, as well as the number of MDHs that received an additional payment under the program. In addition, during this period MDHs varied on other operational and financial metrics, including the share of Medicare revenue coming from inpatient care, various measures of Medicare dependence, and profit margins.

From Fiscal Years 2011 through 2017, the Number of MDHs Declined by 28 Percent, and the Number of MDHs Receiving Additional Payments Decreased by 15 Percent

Our analysis of CMS data shows that the number of MDHs declined from 193 to 138—a 28 percent decrease over the 7-year period from fiscal year 2011 through fiscal year 2017. (See fig. 3.) This decline can be due to a number of factors, including hospital closures, mergers, or changes in designation. For example, we previously reported that 16 MDHs closed between 2013 and 2017. Moreover, our review of Medicare Administrative Contractor documentation found that some MDHs became ineligible for the program due to no longer meeting eligibility criteria. In addition, the number of MDHs that received an additional annual payment also declined, from 92 MDHs in fiscal year 2011 to 78 MDHs in fiscal year 2017—a 15 percent decrease.

Among MDHs that received an additional payment, the amount received and the share of the hospital’s total revenue this payment represented varied widely across the years, though the average amount generally increased over time. (See table 1.) For example, in fiscal year 2017, one hospital received around $1,000 in additional payment while another received almost $10.5 million. While the trend was not uniform among all MDHs, the median additional payment increased from about $695,000 in fiscal year 2011 to about $812,000 in fiscal year 2017.

Our analysis of CMS data also shows that the average additional payment MDHs received ranged from less than 0.1 percent up to 8.7 percent of total facility revenue, with a fairly consistent average of 1.2 to 1.6 percent. (See table 2.) This underscores that the additional payment under the MDH program can be small relative to the overall revenue that the hospital receives.

MDHs Varied over Time on Select Operational and Financial Metrics

Our analysis of CMS data also shows that from fiscal years 2011 through 2017, MDHs varied on selected operational and financial metrics: the mix of Medicare revenue that came from inpatient versus outpatient care, various measures of Medicare dependence, and profit margins.

Inpatient/Outpatient Mix

On average, MDHs experienced a decline in the share of Medicare revenue that came from inpatient services. (See fig. 4.) In fiscal year 2011, around 66 percent of MDH Medicare revenue came from inpatient services compared to 58 percent in fiscal year 2017—a 13 percent decrease. This trend was slightly greater than that for all rural hospitals (an 11 percent decrease) and all hospitals (a 10 percent decrease).

Measures of Medicare Dependence

The trends across three measures of Medicare dependence varied for MDHs over time. Looking at the Medicare share of total revenue for MDHs, we found this share decreased when comparing fiscal years 2011 and 2017, from 25 to 22 percent. (See fig. 5.)

In contrast, in terms of the number of inpatient days and discharges attributable to Medicare beneficiaries, we found these measures both increased slightly over time. Specifically, the median share of MDH inpatient days attributable to Medicare beneficiaries increased, although by less than a percentage point, and the median Medicare share of inpatient discharges increased by about 2 percentage points, when comparing fiscal years 2011 and 2017. (See figures 6 and 7.)

To obtain additional context on the relationship between MDH eligibility criteria and the various measures of Medicare dependence, we ran regression models to identify the extent to which hospitals’ bed size and rural status were associated with the Medicare share of days, discharges, and total care revenue for all hospitals from fiscal years 2011 through 2017. We found that rural hospitals with fewer beds were associated with higher Medicare shares of inpatient days and discharges, holding all other factors constant. This indicates that by targeting smaller, rural hospitals in its eligibility criteria, the MDH program is targeting hospitals that are Medicare-dependent defined in terms of inpatient volume. At the same time, rural hospitals with fewer beds generally received a smaller share of their total care revenue from Medicare compared with other hospitals. This suggests that hospitals associated with high Medicare inpatient volume may not have relatively high shares of total care revenue coming from Medicare. For more technical detail on our regression analyses and findings, see appendix III.

Profit Margins

Our analysis of self-reported data from hospitals shows that Medicare profit margins and total facility profit margins declined for MDHs from fiscal year 2011 through 2017. (See table 3.) The degree to which Medicare margins declined for MDHs during this time period (6 percentage points) was greater than the degree to which they declined for rural hospitals (4 percentage points) and all hospitals (3 percentage points). The self-reported data show that unlike rural and all hospitals, MDHs were not profitable in 2017—meaning that the revenue they received from Medicare and other payers was less than their reported costs for providing services. Specifically, the total facility profit margin turned from positive to negative and dropped almost two percentage points between fiscal years 2011 and 2017.

We also ran regression models to examine the relationship between all hospitals’ total profit margins and the various measures of Medicare dependence. We found that hospitals with a higher Medicare share of total-care revenue had lower total facility margins on average, holding all other factors constant; in contrast, there was no significant relationship between total facility margins and the inpatient volume-based measures of Medicare dependence. This indicates that a higher volume of inpatient services was not associated with lower profitability.

Agency Comments

We provided a draft of this report to the Department of Health and Human Services for comment. The Department of Health and Human Services provided technical comments, which we incorporated as appropriate.

We are sending copies of this report to the Secretary of the Department of Health and Human Services. In addition, this report is available at no charge on the GAO website at http://www.gao.gov.

If you or your staffs have any questions about this report, please contact me at (202) 512-7114 or farbj@gao.gov. Contact points for our Offices of Congressional Relations and Public Affairs may be found on the last page of this report. GAO staff who made key contributions to this report are listed in appendix IV.

Appendix I: Scope and Methodology

This appendix explains the quantitative scope and methodology used to examine how the Medicare-dependent hospital (MDH) designation differs from the other Medicare rural hospital designations. This appendix also explains the scope and methodology used to describe changes in the number and selected metrics of MDHs and other hospital types, including those used for a regression analysis to provide information on the relationship between MDH program criteria and Medicare dependence.

Differences between MDH and Other Designations

To describe how the MDH designation differs from other rural hospital designations, we used CMS data—specifically, the Provider Specific File (PSF)—to identify the number of MDHs, critical access hospitals (CAH), sole community hospitals (SCH), rural low-volume adjustment hospitals (LVA), and rural referral centers (RRC) in fiscal year 2017. We then identified all rural hospitals without a designation in 2017 using the 2018 CMS Inpatient Prospective Payment System (IPPS) Impact File because those data are prepared in the middle of the year preceding the fiscal year. We define rural hospitals using the CMS MDH programmatic definition; that is, those hospitals that are not located in metropolitan statistical areas, as well as those hospitals that reclassified as rural for CMS payment purposes. We next identified the number of hospitals with each designation and the value of additional payments received under the rural designations that each hospital had in that year using data provided by each hospital through their Medicare Cost Report (MCR). The MCR is submitted to CMS by hospitals each fiscal year and contains information such as facility characteristics, utilization data, and costs to provide services to Medicare beneficiaries and all patients. Because CAHs are paid based on cost under a different payment system than the other hospitals, we did not have complete data to estimate what those hospitals would have been paid under the inpatient prospective payment system and thus could not identify the additional payments received by CAHs. In addition, RRCs only receive indirect payment benefits, and thus we could not calculate a comparable additional payment for that group of hospitals. For all analyses, we excluded hospitals within the Indian Health Service, as well as hospitals in Maryland and those outside of the remaining 49 states and the District of Columbia. We also excluded hospitals with reporting periods greater than 14 months or less than 10 months and those that reported zero or negative Medicare revenue.

Number of MDHs and Selected Metrics

To describe changes in the number and select metrics of MDHs and other hospital types, we examined MCR data for fiscal years 2011 through 2017. To first identify the universe of MDHs, rural hospitals, and all acute care inpatient prospective payment system (IPPS) hospitals, we used the PSF and MCR for fiscal years 2011 through 2017, as well as CMS Impact Files for fiscal years 2012 through 2018. Then, we used the MCR to calculate the number of MDHs that received the MDH payment adjustment and the distribution of additional payments among MDHs in each year. Using those same data sources, we then calculated several metrics and examined trends for MDHs as compared to all rural hospitals and all hospitals overall. The first metric is the median proportion of total Medicare payments—referred to as revenue—each hospital group received from providing inpatient and outpatient care to Medicare beneficiaries. The second metric is hospitals’ median profit margins—a profitability measure calculated as the amount of revenue the hospital received minus reported costs, divided by the amount of revenue received. We calculated profit margins specific to Medicare revenue and costs (Medicare profit margins) but also for revenue and costs beyond Medicare (total facility profit margins), including payments for treating non-Medicare (including privately insured) patients. We calculated Medicare and total facility profit margins at the hospital level using hospital-reported costs and revenues from the MCRs, and reported the median margins for each hospital group. The Medicare margin reflects only payments and costs received for inpatient and outpatient services (about 90 percent of total Medicare revenue, according to CMS officials) and excludes payments and costs for other hospital-based services, such as those for skilled nursing and home health care. Third, we calculated hospitals’ degree of Medicare dependence using three separate definitions, or measures, of dependence: (1) the amount of revenue the hospital received from Medicare as a share of all the revenue the hospital received for inpatient and outpatient services (total care revenue), (2) the share of inpatient days of care the hospital provides that are attributed to Medicare beneficiaries, and (3) the share of inpatient discharges that are attributed to Medicare beneficiaries. We also calculated these metrics separately for those MDHs that were eligible for the program based on data from the 1980s—legacy MDHs. To do so, we used data provided by Medicare Administrative Contractors—third-party entities that administer Medicare program payments and determine MDH eligibility.

Regression Analysis

To provide additional context on the relationship between MDH eligibility criteria and the various definitions of Medicare dependence, we developed an econometric model to analyze the association between bed size, rural status, and the three measures of Medicare dependence. We conducted the regression analysis using data from the CMS IPPS Impact Files and MCRs from fiscal years 2011 through 2017. We used the following measures as dependent variables: (1) the amount of revenue the hospital received from Medicare as a share of all the revenue the hospital received for inpatient and outpatient services (total care revenue), (2) the share of inpatient days of care the hospital provides that are attributed to Medicare beneficiaries, and (3) the share of inpatient discharges that are attributed to Medicare beneficiaries.

Dependent Variables

𝑌𝑌𝑖𝑖𝑖𝑖=log (𝑅𝑅𝑖𝑖𝑖𝑖) .

Where 𝑅𝑅𝑖𝑖𝑖𝑖 represents the Medicare share of revenue, inpatient days or discharges, and the i and t subscripts represent the hospital and year, respectively. This formulation has the advantage of restricting the models’ predicted values to be positive and also allows for a relatively straightforward interpretation of the parameter estimates.

Explanatory Variables

We included hospital capacity or size as measured by the number of hospital beds. The number of beds is itself one of the criteria for MDH eligibility, and we were interested in whether hospitals of smaller sizes have more or less Medicare dependency.

We included an indicator variable flagging whether the hospital is in a rural location. Rural location is one of the criteria for MDH program eligibility, and so this was a key variable in our model.

We included the ownership category of the hospital, such as whether a hospital is for-profit or not for-profit, or whether it is a public or private institution. This organizational category may determine institutional characteristics, which affects the likelihood that the hospital serves either more or fewer Medicare beneficiaries.

We included the degree of proximity to other hospitals of substantive size; specifically, the distance from the closest hospital with at least 100 beds. In addition to our rural indicator variable, this controlled for whether more remote hospitals are more likely to be more dependent on Medicare.

We included whether the state in which the hospital is located has expanded Medicaid to provide coverage to low-income, non-elderly adults, because it is possible that an increased number of Medicaid- eligible patients may affect the number of Medicare patients using hospital services. This variable may be associated with less Medicare dependence if Medicaid becomes a relatively larger payer source, or it may be associated with more Medicare dependence if Medicaid eligibility brings Medicare-eligible people into the health care system.

We included the percent of population in the hospital’s county over age 65, because areas with larger numbers of people over age 65 may be more likely to have a higher proportion of Medicare beneficiaries using health care services.

We included the percent growth in county population, which allowed us to control for areas with declining populations that may be more likely to contain Medicare-dependent hospitals.

Our model included time fixed effects (a dummy variable for each year in the analysis). The time fixed effects controlled for factors affecting hospitals nationally in as given year—in particular, those factors for which data were unavailable.

We included a set of state fixed effects (a dummy variable for each of the states in the analysis) to control for effects that are common to a specific area, but for which data may have been unavailable.

We estimated specifications that included interactions between our bed size categories and rural location. This allowed us to determine whether bed size had the same impact on Medicare dependence for hospitals in rural locations compared with those in urban locations.

Model Specification

ln (𝑅𝑅𝑖𝑖𝑖𝑖)=�𝑓𝑓𝑖𝑖𝐹𝐹𝑖𝑖 +𝑋𝑋𝑖𝑖𝑖𝑖𝛽𝛽+𝐶𝐶𝑠𝑠𝑖𝑖𝛾𝛾+𝜀𝜀𝑖𝑖𝑖𝑖,𝑡𝑡=1,…,𝑇𝑇; 𝑖𝑖=1,…,𝐻𝐻.

The dependent variable is the logarithm of our measure of Medicare

𝑋𝑋𝑖𝑖𝑖𝑖 is a 1 x k vector of hospital characteristics and possible 𝛽𝛽 is a k x 1 vector of parameters associated with the hospital interactions of these characteristics, where i denotes the ith hospital ∑ ∑

𝐶𝐶𝑠𝑠𝑖𝑖 is a 1 x m vector of time-varying county-level variables hospital and their associated (lower case) parameters. 𝛾𝛾 is an m x 1 vector of parameters associated with the state-level characteristics such as the percent of the population over 65 and the dependence, 𝑅𝑅𝑖𝑖𝑖𝑖. and t denotes the year. 𝑋𝑋𝑖𝑖𝑖𝑖 contains key explanatory variables such as characteristics, 𝑋𝑋𝑖𝑖𝑖𝑖. ownership type, the number of beds, rural or urban location, whether 𝑇𝑇𝑖𝑖=2a hospital receives MDH program monies and other characteristics. represents the set of time (year) dummy variables (upper 𝑆𝑆𝑠𝑠=2 represents the set of state dummy variables (upper case) case) and their associated (lower case) parameters. characteristics such as the percent of the population over 65 and the county population growth rate. characteristics, 𝐶𝐶𝑐𝑐𝑖𝑖.

Our model includes an interaction effect between the rural dummy variable and each of the characteristics except the geographic fixed effects. 𝜀𝜀𝑖𝑖𝑖𝑖 is a well-behaved Gaussian random error term that may have a heteroskedastic and/or clustered structure.

We used Stata® to estimate the regression model, using fixed effects at the state-level to account for unobserved heterogeneity and clustering at the county-level.

Specification of the Bed Size Categories and Geographic Fixed Effects

Our focus was on the main criteria for MDH eligibility—namely hospital size as measured by number of beds and rural versus no-rural hospital location. We divided the hospitals into five bed number categories:

50 beds or fewer

Over 50 beds to 100 beds

Over 100 beds to 300 beds

Over 300 beds to 400 beds This categorization strikes a balance between having too many categories, which would reduce the statistical power of our analysis, and having too few categories, which would fail to identify any non-linear pattern in the statistical relationship. These categories also contain the 100 bed criterion as one of the cut-off points.

Our analysis controls for location and possible heterogeneity by using geographic fixed effects but we also want to identify the impact of rural location. Selecting too detailed a level of geographic fixed effect such as county or zip code would limit our ability to identify the rural effect so we used states. We recognized that state fixed effects may not identify more localized effects; this is a limitation of our model.

Total Facility Profit Margins and Measures of Medicare Dependence

We also modeled the effects of hospital characteristics on total facility profit margins; that is, the difference between revenue and costs as a percent of revenue. For MDHs in our analysis, we excluded any MDH additional payment from the margin calculation in order to isolate and remove the program impact on financial status.

We used the same explanatory factors in our econometric model of hospital margins as in our models of Medicare dependence but we supplement these factors with our three measures of Medicare dependence—a separate model for each measure. This allowed us to assess how our different measures of Medicare dependence are associated with financial well-being.

We assessed the reliability of the relevant fields in each of the data sets we used for these analyses by interviewing CMS officials, reviewing related documentation, and performing data checks. On the basis of these steps, we concluded that the data were sufficiently reliable for the purposes of our reporting objective.

Appendix II: Medicare Rural Hospital Payment Designation Eligibility and Payment

We identified five Medicare rural hospital payment designations and categorized them into two categories: (1) primary payment designations and (2) secondary payment designations. Primary designations include critical access hospitals (CAH), sole community hospitals (SCH), and Medicare-dependent Hospitals (MDH). Each designation has distinct eligibility requirements and payment methodologies.

Appendix III: Full Regression Results

This appendix describes the full results for our modeling of Medicare dollars as a percentage of total revenue, the percent of inpatient days, the percent of inpatient discharges, and total hospital profit margins.

Results for Modeling Medicare Revenue as a Share of Total Revenue

We tested for the hypothesis that key groups of parameters were significantly different between urban and rural locations.

We performed a k-parameter post-estimation Wald linear restriction where 𝛽𝛽𝑘𝑘𝑢𝑢 and 𝛽𝛽𝑘𝑘𝑟𝑟 are matrices of the estimated urban and rural parameters, respectively, for each of the k categories (bed-size, ownership type, etc.). We rejected the null hypothesis of parameter equality for bed-size, ownership types, Medicaid expansion, and year dummies at the 5 percent level. The miles distance parameters rejected the hypothesis at marginally above the 5 percent level.

Rural hospitals generally were associated with larger Medicare shares of revenue than urban hospitals. In every bed-size category, the parameters for rural hospitals were significantly greater than for urban hospitals. In addition, controlling for urban-rural location, with the exception of the largest hospital category (over 400 beds) hospitals with fewer beds had a smaller Medicare share of revenue, as shown in figure 8.

Hospitals in counties with higher percentages of people over age 65 were significantly associated with greater Medicare dependence.

Results for Modeling Medicare as a Share of Total Inpatient Days

Our Wald tests rejected the null that parameters rural and urban were equal in the bed-number categories and in the ownership categories.

As with the Medicare share of total revenue, our model for Medicare share of inpatient days showed, controlling for bed numbers, that rural hospitals generally had significantly greater Medicare dependence than urban hospitals. In most bed-size categories, the parameters for rural hospitals were greater than for urban hospitals.

The pattern for bed size was different for Medicare dependence measured in revenue in that for rural hospitals, dependence fell as bed numbers rose, but, for urban hospitals, we observed a hump- shape distribution with the middle bed-number categories having higher dependence than the smallest and largest categories, as shown in figure 9.

Hospitals located in counties with higher percentages of people over age 65 had higher dependence.

Results for Modeling Medicare as a Share of Total Inpatient Discharges

Our model for the Medicare share of inpatient discharges showed that, controlling for bed numbers, rural hospitals generally had greater Medicare dependence than urban hospitals.

In most bed-size categories, the parameters for rural hospitals were significantly greater than for urban hospitals. Our Wald tests rejected the null hypothesis that parameters for rural and urban were equal in the bed-size categories, Medicaid expansion variables, and in the ownership categories.

The pattern for bed numbers was also different to Medicare dependence measured in revenue. The urban hospitals had a hump- shape distribution with the middle bed-number categories having higher dependence than the smallest and largest categories, whereas the rural showed largest effects at the smallest and the larger intermediate categories, as shown in figure 10.

Hospitals located in counties with higher percentages of people over age 65 had higher dependence.

Results for Modeling Hospital Profit Margins

The Medicare share of total revenue was significantly associated with smaller total facility profit margins and was the only statistically significant measure of Medicare dependence in the margin models. In general, hospitals with small numbers of beds—fewer than 100—were associated with smaller hospital margins relative to our reference category of large urban hospitals. However, there was no significant difference in any of the bed-number categories between urban and rural hospitals.

Appendix IV: GAO Contact and Staff Acknowledgments

GAO Contact Staff Acknowledgments

Jessica Farb, (202) 512-7114 or farbj@gao.gov In addition to the contact named above, Gregory Giusto (Assistant Director), Kate Nast Jones (Analyst-in-Charge), Britt Carlson, Rachel Gringlas, Michael Kendix, Vikki Porter, Caitlin Scoville, Jennifer Rudisill, and Jeffrey Tamburello made key contributions to this report.