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«The Effect of Competition on Nursing Home Expenditures under Prospective Reimbursement John A. Nyman Thefor-profit nursing home's incentive to ...»

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The Effect of Competition

on Nursing Home Expenditures

under Prospective Reimbursement

John A. Nyman

Thefor-profit nursing home's incentive to minimize costs has been maligned as a

major cause of the quality probkms that have traditionally plagued the nursing

home care industry. Yet, profit-maximizing firms in other industries are able to

produce products of adequate quality. In most other industries, however, firms are

constrainedfrom reducing costs to the point where quality suffers by the threat of losing business to competingfirms. In the nursing home industry, competitionfor patients often does not exist because of the shortage of nursing home beds. As a result, one would expect that nursing homes located in areas where there is excess demand would spend less on patient care than homes located where the bed supply is relatively abundant. This hypothesis is tested using Wisconsin datafrom 1983. It is found that, in counties with relatively tight bed supplies, an additional empty bed in all the homes in the county willforce each home to increase expenditures by $. 62 per day for each patient in the home. Overall, the average nursing home located in underbedded markets would spend $5.12 more per patient day or about $240, 000 more annually (in 1983 dollars) ifit were located in a market where it was forced to compete for patients. The implications for public policy are discussed.

Nursing home policy in the 1970s was preoccupied with improving quality. One major cause of the quality problem, as identified by a comprehensive Senate report (1974), was that proprietary nursing homes were lowering the quality of care in order to reduce costs. Since the common form of Medicaid reimbursement in the early 1970s tended to be a flat rate, reducing costs meant greater profit margins.

Many conduded from that report that the profit motive is not consisAddress correspondence and requests for reprints toJohn A. Nyman, Ph.D., Assistant Professor, Center for Health Services Research, The University of Iowa, Iowa City, IA 52242.

HSR: Health Services Research 23:4 (October 1988) tent with assurance of adequate quality nursing home care and that the federal government should only approve reimbursement for costs actually incurred. The latter conclusion was embodied in a federal requirement (U.S. Congress 1972) that nursing homes be reimbursed on a "reasonable cost-related basis."

While it is true that the profit motive in nursing homes is an incentive to minimize costs, it is also true that firms in most markets face similar incentives. In typical markets, however, if a firm reduces costs to the extent that quality suffers, it will generally lose business to rival firms. Thus, typical firms are prohibited-by the presence of competitors willing and able to serve new customers-from reducing costs to a point where quality is jeopardized. Indeed, this competition is required in free markets to offset the cost-minimization incentive and ensure that the products supplied on the market are of adequate quality.

In many of the nursing home care markets of the 1970s (as well as the 1980s), the requisite competition seems to have been lacking because of an excess demand for beds. Scanlon (1979) has argued that excess demand is a pervasive feature of nursing home markets in the United States. Vladeck (1980) alludes to a shortage of beds at many points in his historical review of nursing home policy. When excess demand for beds exists, nursing homes can reduce costs - and thus quality - with impunity because prospective patients (especially Medicaid patients) are forced to accept the first home with an empty bed, regardless of quality. If a shortage exists to the extent that Scanlon argues is true and has persisted to the extent that Vladeck appears to support, then a more fundamental cause of the quality problems that nursing homes traditionally face is a lack of competition caused by an excess demand for beds.

If excess demand permits nursing homes to lower costs with impunity, then we would expect that homes located in markets where the bed supply is tight will have lower per unit costs than those located in markets where there is a surplus of beds. Although the health services literature contains many studies that estimate nursing home cost functions (e.g., Ullmann 1985; Schlenker and Shaughnessy 1984; Palm and Nelson 1984; Meiners 1982; Birnbaum et al. 1981; Ruchlin and Levey 1972; Lee and Birnbaum 1983; Bishop 1980; Smith and Fottler 1981; Koetting 1980; Caswell and Cleverly 1983; Walsh 1979;

Ullmann 1984; Christianson 1979; Smith et al. 1985; Schlenker 1986), none of them has included an explanatory variable measuring the degree of excess demand in the market in which the nursing home is located.' This article explicitly tests for a relationship between excess Competition and Nursing Home Expenditures market demand and nursing home expenditures using 1983 data from prospectively reimbursed Wisconsin nursing homes. In the first section, the data sets and empirical model are described. In the second, the results are reported. And in the concluding section, the implications for public policy are discussed.

DATA AND MODEL

Data are from the 1983 Wisconsin Annual Survey of Nursing Homes, which Wisconsin requires nursing homes to complete as part of the annual requirements for Medicaid recertification. This data set contains information on 475 nursing homes (according to the number of unique federal identifiers). These data have been supplemented with information from the 1983 Medicaid cost reports and 1983 data on the violations of Wisconsin's Medicaid certification code. In merging these three data sets, about 125 observations were lost because the identifiers used to link the different data sets did not match. In addition, the data were constrained to include only observations that showed a (meaningful) skilled nursing facility (SNF) Medicaid reimbursement rate. This excluded about 80 observations representing mostly the nursing homes that contained only intermediate care facility (ICF) patients. Ultimately, 269 nursing homes were used in the statistical analysis.





Regression analysis was used to test for a relationship between nursing home expenditures and excess demand. The dependent variable was the cost of nursing home care per patient day, that is, average costs. This figure was calculated by adding figures from the four cost centers in Wisconsin's Medicaid cost report: direct care costs, fuel and utility expenditures, property tax or municipal fees, and social services costs, all per patient day. This aggregated cost variable was regressed on the 13 independent variables described below.

Excess demand, the first independent variable, was not measured directly since the data set did not contain information on the number of different patients wishing to enter homes in a market. Instead, the average number of empty beds in the county in which the nursing home was located was used as a proxy measure for excess demand.

This variable was intended to capture the relative need to compete for patients based on exogenous market conditions. Homes located in counties where all homes were completely occupied would not need to compete for patients, but homes located in counties where there were many empty beds on average would need to compete.

The average number of empty beds, rather than the average perHSR: Health Services Research 23:4 (October 1988) centage, was used because the number of empty beds may be a better measure of the need to compete from the perspective of the total number of beds available in the market. For example, one county might have five 50-bed homes with 2 empty beds in each, and another might have five 500-bed homes with 20 empty beds in each. Although the percentage of empty beds is the same for homes located in each county, 4 percent, the total number of empty beds available, 10 and 100 respectively, differs significantly. Other things being equal, we would expect it to be easier for firms to find patients in the former county than in the latter; thus, nursing homes located in the former county would be less likely to feel the need to compete for patients. The average number of empty beds, 2 and 20 respectively, would reflect this difference, whereas the average percentage of empty beds would not.

It is not necessary that all beds be filled for excess demand to exist.

Normal turnover means that some beds are empty regardless of demand conditions. Furthermore, nursing homes may leave some beds empty rather than filling them with Medicaid patients, because they want to be able to accommodate the more lucrative private patients (Bishop 1979). The mean and standard deviation of the average county occupancies for these homes is about 6.5 and 3.75 empty beds, respectively. The mean number of actual beds is about 135. Given that a substantial number of nursing homes are located in counties that averaged less than three empty beds per home, it would be reasonable to assume that excess demand did exist in at least some Wisconsin counties in 1983.

The average number of empty beds in the county as a measure of the need to compete for patients is a characteristic of the market environment in which the firm is located. An individual home's excess capacity will contribute to this variable, but the number of empty beds in an individual home is likely to be different, either larger or smaller, than the average excess capacity of all the homes in the county. Nevertheless, the two variables may be correlated to some extent, and a greater number of empty beds in the individual homes might directly contribute to increased costs per patient day because the fixed costs in such homes are spread over fewer patients (and patient days). It is, therefore, necessary to control for this factor in order to ensure that the average number of empty beds truly represents an exogenous market variable. Accordingly, a variable representing the number of empty beds in the individual home was included in the regression. In the following discussion, the average number of empty beds in the county will be referred to as "average excess capacity," while the number of Competition and Nursing Home Expenditures 559 empty beds in an individual home will be called simply 'excess capacity."

The dependent variable, costs per patient day, will vary with the quality of care provided and the dependency of the patients served. In order to control for cost differences along these dimensions of the output, measures of quality and case mix were included in the regression. Quality is represented by the number of violations of the Medicaid code weighted according to severity. The severity weights represent the relative magnitudes of the maximum fines-1, 10, and 50-that are associated with each violation type- C, B, and A, respectively. The characteristics of quality reflected by the 1983 violations measure, however, may not be representative of all of the qualitydefining characteristics of the nursing home. This is because the fines associated with the violation characteristics force the home to be more concerned about these characteristics than about the many others that patients might find desirable. For example, a home is more likely to have the requisite staffing ratios than to have staff who treat patients with kindness, because the former characteristic is subject to fine whereas the latter one is not. Therefore, we cannot assume that we have adequately controlled for quality of care by having included this variable in the regression.

Case-mix differences were measured by two variables: the home's average activities of daily living (ADL) score and the average length of stay of patients in the home. The home's average ADL score was calculated by adding the number of patients who are dependent in each of the eight ADL categories (bathing, bowel continence, urine continence, mobility, dressing, feeding, toileting, and transferring) and dividing by the number of patients in the home. For example, a value of 8 for this variable would represent a home with all patients dependent in all eight categories.

Average length of stay, the second case-mix variable, was constructed by taking the number of patients residing in the home less than one year, the number of patients in the home between one and two years, between two and three years, three and four years, four and five years, and over five years, and multiplying each number by.5, 1.5, 2.5, 3.5, 4.5, and 5.5, respectively. These products were then summed and divided by the total number of patients, yielding a variable that measures the average length of stay of patients in the home on a given date. Homes that have a larger number of short-stay patients may have more acutely ill patients whose health can be expected to improve. As patients improve, they consume fewer care resources;

therefore, homes with more of these kinds of patients may have lower HSR: Health Services Research 23:4 (October 1988) costs. Because chronic patients do not get better, it is expected that the longer the average length of stay, the greater the number of chronic patients and the greater the costs.

Average costs are assumed to vary with the size of the output. We adopt the convention of the health services cost-function literature and measure output by the number of beds in the home rather than by the number of patient days in the year or the average number of patients served. This is a useful convention because it allows for the direct estimation of the optimal bed size in nursing homes. It is expected that a U-shaped relationship will exist; therefore, a squared term was also introduced into the regression in order to accommodate this functional form.

The eighth explanatory variable is a dummy variable representing whether the nursing home was or was not chartered as a for-profit nursing home. For-profit nursing homes have explicit reasons for minimizing costs and are therefore likely to have lower costs than nonprofit homes. Nonprofits are constrained to have costs equal revenues in the long run. Nevertheless, in the short run, nonprofits may desire revenue surpluses in order to build up a fund for an investment, such as a capital improvement, that would enhance the prestige of the nursing home or achieve some other objective. If nonprofit firms do want surpluses, then their desire to minimize costs may differ only qualitatively from the similar desire in for-profits. They may, therefore, take advantage of the same market failures that for-profits do, although to a lesser extent.



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